Publications

AI-based systems for visual scene understanding benefit from a large field of view (FOV). Multiple camera systems extend the FOV, but larger and higher-quality images strain acquisition, communication, and computing resources. Sub-sampling the FOV effectively addresses this challenge without compromising performance on complex tasks that require fine visual cues and contextual information. We demonstrate that a variable sampling scheme, inspired by human vision, outperforms uniform sampling in several visual question answering (VQA) tasks with a limited sample budget (3% of full resolution). Specifically, we show accuracy gains of 3.7%, 2.0%, and 0.9% on the GQA, VQAv2, and SEED-Bench datasets, respectively. This improvement, achieved without image scanning, holds regardless of the fixation point location, as confirmed by control experiments. The results show the potential of the biologically inspired image representation to improve the design of visual acquisition and processing models in future AI systems.

Diabetic Retinopathy (DR) is a common complication of diabetes that, in severe cases, can result in blindness. Accurate clinical treatment is imperative to prevent these cases and relies considerably on an exact diagnosis of the various symptoms of DR. We aim to advance DR diagnosis by providing a practical tool to automatically classify Optical Coherence Tomography (OCT) scans for DR and to identify and localize DR-related morphological features within the scans. Our system obtains raw OCT input and only sparse clinical annotations at the volume level, which can be obtained automatically from routine electronic medical records.We developed a novel neural network architecture, OCT-Transformer, that obtains state-of-the-art classification results compared to previous models and does so with limited training data. We base our architecture on an attention mechanism and show this to be the driving factor for the boost in performance. We additionally use our model to locate pixels within the input scans that explain its classification.

Humans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

We study the task of finding good local optima in combinatorial optimization problems. Although combinatorial optimization is NP-hard in general, locally optimal solutions are frequently used in practice. Local search methods however typically converge to a limited set of optima that depend on their initialization. Sampling methods on the other hand can access any valid solution, and thus can be used either directly or alongside methods of the former type as a way for finding good local optima. Since the effectiveness of this strategy depends on the sampling distribution, we derive a robust learning algorithm that adapts sampling distributions towards good local optima of arbitrary objective functions. As a first use case, we empirically study the efficiency in which sampling methods can recover locally maximal cliques in undirected graphs. Not only do we show how our adaptive sampler outperforms related methods, we also show how it can even approach the performance of established clique algorithms. As a second use case, we consider how greedy algorithms can be combined with our adaptive sampler, and we demonstrate how this leads to superior performance in k-medoid clustering. Together, these findings suggest that our adaptive sampler can provide an effective strategy to combinatorial optimization problems that arise in practice.

Patterns are broad phenomena that relate to biology, chemistry, and physics. The dendritic growth of crystals is the most well-known ice pattern formation process. Tyndall figures are water-melting patterns that occur when ice absorbs light and becomes superheated. Here, we report a previously undescribed ice and water pattern formation process induced by near-infrared irradiation that heats one phase more than the other in a two-phase system. The pattern formed during the irradiation of ice crystals tens of micrometers thick in solution near equilibrium. Dynamic holes and a microchannel labyrinth then formed in specific regions and were characterized by a typical distance between melted points. We concluded that the differential absorption of water and ice was the driving force for the pattern formation. Heating ice by laser absorption might be useful in applications such as the cryopreservation of biological samples.

Despite a large body of research on response properties of neurons in the inferior temporal (IT) cortex, studies to date have not yet produced quantitative feature descriptions that can predict responses to arbitrary objects. This deficit in the research prevents a thorough understanding of object representation in the IT cortex. Here we propose a fragment-based approach for finding quantitative feature descriptions of face neurons in the IT cortex. The development of the proposed method was driven by the assumption that it is possible to recover features from a set of natural image fragments if the set is sufficiently large. To find the feature from the set, we compared object responses predicted from each fragment and responses of neurons to these objects, and search for the fragment that revealed the highest correlation with neural object responses. Prediction of object responses of each fragment was made by normalizing Euclidian distance between the fragment and each object to 0 to 1 such that the smaller distance gives the higher value. The distance was calculated at the space where images were transformed to a local orientation space by a Gabor filter and a local max operation. The method allowed us to find features with a correlation coefficient between predicted and neural responses of 0.68 on average (number of object stimuli, 104) from among 560,000 feature candidates, reliably explaining differential responses among faces as well as a general preference for faces over to non-face objects. Furthermore, predicted responses of the resulting features to novel object images were significantly correlated with neural responses to these images. Identification of features comprising specific, moderately complex combinations of local orientations and colors enabled us to predict responses to upright and inverted faces, which provided a possible mechanism of face inversion effects. (292/300).

The goal in this work is to model the process of 'full interpretation' of object images, which is the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is approached by dividing the interpretation of the complete object to the interpretation of multiple reduced but interpretable local regions. In such reduced regions, interpretation is simpler, since the number of semantic components is small, and the variability of possible configurations is low.We model the interpretation process by identifying primitive components and relations that play a useful role in local interpretation by humans. To identify useful components and relations used in the interpretation process, we consider the interpretation of 'minimal configurations': these are reduced local regions, which are minimal in the sense that further reduction renders them unrecognizable and uninterpretable. We show that such minimal interpretable images have useful properties, which we use to identify informative features and relations used for full interpretation. We describe our interpretation model, and show results of detailed interpretations of minimal configurations, produced automatically by the model. Finally, we discuss possible extensions and implications of full interpretation to difficult visual tasks, such as recognizing social interactions, which are beyond the scope of current models of visual recognition.

In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.

Prominent theories of action recognition suggest that during the recognition of actions the physical patterns of the action is associated with only one action interpretation (e.g., a person waving his arm is recognized as waving). In contrast to this view, studies examining the visual categorization of objects show that objects are recognized in multiple ways (e.g., a VW Beetle can be recognized as a car or a beetle) and that categorization performance is based on the visual and motor movement similarity between objects. Here, we studied whether we find evidence for multiple levels of categorization for social interactions (physical interactions with another person, e.g., handshakes). To do so, we compared visual categorization of objects and social interactions (Experiments 1 and 2) in a grouping task and assessed the usefulness of motor and visual cues (Experiments 3, 4, and 5) for object and social interaction categorization. Additionally, we measured recognition performance associated with recognizing objects and social interactions at different categorization levels (Experiment 6). We found that basic level object categories were associated with a clear recognition advantage compared to subordinate recognition but basic level social interaction categories provided only a little recognition advantage. Moreover, basic level object categories were more strongly associated with similar visual and motor cues than basic level social interaction categories. The results suggest that cognitive categories underlying the recognition of objects and social interactions are associated with different performances. These results are in line with the idea that the same action can be associated with several action interpretations (e.g., a person waving his arm can be recognized as waving or greeting).

For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, and tolerance for image transformations was primarily achieved by using robust feature vectors. In this paper we propose a novel and computationally efficient way to learn a local Mahalanobis metric per datum, and show how we can learn a local invariant metric to any transformation in order to improve performance.

We consider the task of visual recognition of objects and their parts in a dynamic environment, where the appearances, as well as the relative positions between parts, change over time. We start with a model of an object class learned from a limited set of view directions (such as side views of cars or airplanes). The algorithm is then given a video input which contains the object moving and changing its viewing direction. Our aim is to reliably detect the object as it changes beyond its known views, and use the dynamically changing views to extend the initial object model. To achieve this goal, we construct an object model at each time instant by combining two sources: consistency with the measured optical flow, together with similarity to the object model at an earlier time. We introduce a simple new way of updating the object model dynamically by combining approximate nearest neighbors search with kernel density estimation. Unlike tracking-by-detection methods that focus on tracking a specific object over time, we demonstrate how the proposed method can be used for learning, by extending the initial generic object model to cope with novel viewing directions, without further supervision. The results show that the adaptive combination of the initial model with even a single video sequence already provides useful generalization of the class model to novel views.

Existing classification algorithms use a set of training examples to select classification features, which are then used for all future applications of the classifier. A major problem with this approach is the selection of a training set: a small set will result in reduced performance, and a large set will require extensive training. In addition, class appearance may change over time requiring an adaptive classification system. In this paper, we propose a solution to these basic problems by developing an on-line feature selection method, which continuously modifies and improves the features used for classification based on the examples provided so far. The method is used for learning a new class, and to continuously improve classification performance as new data becomes available. In ongoing learning, examples are continuously presented to the system, and new features arise from these examples. The method continuously measures the value of the selected features using mutual information, and uses these values to efficiently update the set of selected features when new training information becomes available. The problem is challenging because at each stage the training process uses a small subset of the training data. Surprisingly, with sufficient training data the on-line process reaches the same performance as a scheme that has a complete access to the entire training data.

Keywords: Neurosciences

Class learning, both supervised and unsupervised, requires feature selection, which includes two main components. The first is the selection of a discriminative subset of features from a larger pool. The second is the selection of detection parameters for each feature to optimize classification performance. In this paper we present a method for the discovery of multiple classification features, their detection parameters and their consistent configurations, in the Ally unsupervised setting. This is achieved by a global optimization of joint consistency between the features as a function of the detection parameters, without assuming any prior parametric model. We demonstrate how the proposed framework can be applied for learning different types of feature parameters, such as detection thresholds and geometric relations, resulting in the unsupervised discovery of informative configurations of objects parts. We test our approach on a wide range of classes and show good results. We also demonstrate how the approach can be used to unsupervisedly separate and learn visually similar subclasses of a single category such as facial views or hand poses. We use the approach to compare various criteria for feature consistency, including Mutual Information, Suspicious Coincidence, L2 and Jaccard index. Finally, we compare our approach to a parametric consistency optimization technique such as pLSA and show significantly better performance.

We consider the problem of extracting features for multi-class recognition problems. The features are required to make fine distinctions between similar classes, combined with tolerance for distortions and missing information. We define and compare two general approaches, both based on maximizing the delivered information for recognition: one divides the problem into multiple binary classification tasks, while the other uses a single multi-class scheme. The two strategies result in markedly different sets of features, which we apply to face identification and detection. We show that the first produces a sparse set of distinctive features that are specific to an individual face, and are highly tolerant to distortions and missing input. The second produces compact features, each shared by about half of the faces, which perform better in general face detection. The results show the advantage of distinctive features for making fine distinctions in a robust manner. They also show that different features are optimal for recognition tasks at different levels of specificity.

We develop a novel method for class-based feature matching across large changes in viewing conditions. The method is based on the property that when objects share a similar part, the similarity is preserved across viewing conditions. Given a feature and a training set of object images, we first identify the subset of objects that share this feature. The transformation of the feature's appearance across viewing conditions is determined mainly by properties of the feature, rather than of the object in which it is embedded. Therefore, the transformed feature will be shared by approximately the same set of objects. Based on this consistency requirement, corresponding features can be reliably identified from a set of candidate matches. Unlike previous approaches, the proposed scheme compares feature appearances only in similar viewing conditions, rather than across different viewing conditions. As a result, the scheme is not restricted to locally planar objects or affine transformations. The approach also does not require examples of correct matches. We show that by using the proposed method, a dense set of accurate correspondences can be obtained. Experimental comparisons demonstrate that matching accuracy is significantly improved over previous schemes. Finally, we show that the scheme can be successfully used for invariant object recognition.

Current object recognition systems aim at recognizing numerous object classes under limited supervision conditions. This paper provides a benchmark for evaluating progress on this fundamental task. Several methods have recently proposed to utilize the commonalities between object classes in order to improve generalization accuracy. Such methods can be termed interclass transfer techniques. However, it is currently difficult to asses which of the proposed methods maximally utilizes the shared structure of related classes. In order to facilitate the development, as well as the assessment of methods for dealing with multiple related classes, a new dataset including images of several hundred mammal classes, is provided, together with preliminary results of its use. The images in this dataset are organized into five levels of variability, and their labels include information on the objects' identity, location and pose. From this dataset, a classification benchmark has been derived, requiring fine distinctions between 72 mammal classes. It is then demonstrated that a recognition method which is highly successful on the Caltech101, attains limited accuracy on the current benchmark (36.5%). Since this method does not utilize the shared structure between classes, the question remains as to whether interclass transfer methods can increase the accuracy to the level of human performance (90%). We suggest that a labeled benchmark of the type provided, containing a large number of related classes is crucial for the development and evaluation of classification methods which make efficient use of interclass transfer.

We present a novel method for unsupervised classification., including the discovery of a new category and precise object and part localization. Given a set of unlabelled images, some of which contain an object of an unknown category, with unknown location and unknown size relative to the background, the method automatically identifies the images that contain the objects, localizes them and their parts, and reliably learns their appearance and geometry for subsequent classification. Current unsupervised methods construct classifiers based on a fixed set of initial features. Instead, we propose a new approach which iteratively extracts new features and re-learns the induced classifier, improving class vs. non-class separation at each iteration. We develop two main tools that allow this iterative combined search. The first is a novel star-like model capable of learning a geometric class representation in the unsupervised setting. The second is learning of "part specific features" that are optimized for parts detection, and which optimally combine different part appearances discovered in the training examples. These novel aspects lead to precise part localization and to improvement in overall classification performance compared with previous methods. We applied our method to multiple object classes from Caltech-101, UIUC and a sub-classification problem from PASCAL. The obtained results are comparable to state-of-the-art supervised classification techniques and superior to state-of-the-art unsupervised approaches previously applied to the same image sets.

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.

This paper describes the construction and use of a novel representation for the recognition of objects and their parts, the semantic hierarchy. Its advantages include improved classification performance, accurate detection and localization of object parts and sub-parts, and explicitly identifying the different appearances of each object part. The semantic hierarchy algorithm starts by constructing a minimal feature hierarchy and proceeds by adding semantically equivalent representatives to each node, using the entire hierarchy as a context for determining the identity and locations of added features. Part detection is obtained by a bottom-up top-down cycle. Unlike previous approaches, the semantic hierarchy learns to represent the set of possible appearances of object parts at all levels, and their statistical dependencies. The algorithm is fully automatic and is shown experimentally to substantially improve the recognition of objects and their parts.

We show that the discrimination between visually similar classes often depends on the detection of so-called 'satellite features'. These are local features which are not informative by themselves, and can only be detected reliably at locations specified relative to other features. This makes satellite features difficult to extract by current classification methods. We describe a novel scheme which can extract discriminative satellite features and use them to distinguish between visually similar classes. The algorithm first searches for a set of features ("anchor features") that can be found in all the similar classes. Such features can be detected because the classes are visually similar. The anchors are used to determine the locations of satellite features, which are extracted during learning and used in classification to distinguish between the similar classes. The algorithm is fully automatic, and is shown to work well for many categories of visually similar classes.

We describe a general framework for online multiclass learning based on the notion of hypothesis sharing. In our framework sets of classes are associated with hypotheses. Thus, all classes within a given set share the same hypothesis. This framework includes as special cases commonly used constructions for multiclass categorization such as allocating a unique hypothesis for each class and allocating a single common hypothesis for all classes. We generalize the multiclass Perceptron to our framework and derive a unifying mistake bound analysis. Our construction naturally extends to settings where the number of classes is not known in advance but, rather, is revealed along the online learning process. We demonstrate the merits of our approach by comparing it to previous methods on both synthetic and natural datasets.

Cortical maps and feedback connections are ubiquitous features of the visual cerebral cortex. The role of the feedback connections, however, is unclear. This study was aimed at revealing possible organizational relationships between the feedback projections from area V2 and the functional maps of orientation and retinotopy in area V1. Optical imaging of intrinsic signals was combined with cytochrome oxidase histochemistry and connectional anatomy in owl monkeys. Tracer injections were administered at orientation-selective domains in regions of pale and thick cytochrome oxidase stripes adjacent to the border between these stripes. The feedback projections from V2 were found to be more diffuse than the intrinsic horizontal connections within V1, but they nevertheless demonstrated clustering. The clusters of feedback axons projected preferentially to interblob cytochrome oxidase regions. The distribution of preferred orientations of the recipient domains in V1 was broad but appeared biased toward values similar to the preferred orientation of the projecting cells in V2. The global spatial distribution of the feedback projections in V1 was anisotropic. The major axis of anisotropy was systematically parallel to a retinotopic axis in V1 corresponding to the preferred orientation of the cells of origin in V2. We conclude that the feedback connections from V2 to V1 might play a role in enhancing the response in V1 to collinear contour elements.

We describe a novel technique for identifying semantically equivalent parts in images belonging to the same object class, (e.g. eyes, license plates, aircraft wings etc.). The visual appearance of such object parts can differ substantially, and therefore traditional image similarity-based methods are inappropriate for this task. The technique we propose is based on the use of common context. We first retrieve context ftagments, which consistently appear together with a given input fragment in a stable geometric relation. We then use the context fragments in new images to infer the most likely position of equivalent parts. Given a set of image examples of objects in a class, the method can automatically learn the part structure of the domain - identify the main parts, and how their appearance changes across objects in the class. Two applications of the proposed algorithm are shown: the defection and identification of object parts and object recognition.

We describe an object classification method that can learn from a single training example. In this method, a novel class is characterized by its similarity to a number of previously learned, familiar classes. We demonstrate that this similarity is well-preserved across different class instances. As a result, it generalizes well to new instances of the novel class. A simple comparison of the similarity patterns is therefore sufficient to obtain useful classification performance from a single training example. The similarity between the novel class and the familiar classes in the proposed method can be evaluated using a wide variety of existing classification schemes. It can therefore combine the merits of many different classification methods. Experiments on a database of 107 widely varying object classes demonstrate that the proposed method significantly improves the performance of the baseline algorithm.

In performing recognition, the visual system shows a remarkable capacity to distinguish between significant and immaterial image changes, to learn from examples to recognize new classes of objects, and to generalize from known to novel objects. Here we focus on one aspect of this problem, the ability to recognize novel objects from different viewing directions. This problem of view-invariant recognition is difficult because the image of an object seen from a novel viewing direction can be substantially different from all previously seen images of the same object. We describe an approach to view-invariant recognition that uses extended features to generalize across changes in viewing directions. Extended features are equivalence classes of informative image fragments, which represent object parts under different viewing conditions. This representation is extracted during learning from images of moving objects, and it allows the visual system to generalize from a single view of a novel object, and to compensate for large changes in the viewing direction, without using three-dimensional information. We describe the model, its implementation and performance on natural face images, compare it to alternative approaches, discuss its biological plausibility, and its extension to other aspects of visual recognition. The results of the study suggest that the capacity of the recognition system to generalize to novel conditions in an efficient and flexible manner depends on the ongoing extraction of different families of informative features, acquired for different tasks and different object classes.

We describe a new approach for learning to perform classbased segmentation using only unsegmented training examples. As in previous methods, we first use training images to extract fragments that contain common object parts. We then show how these parts can be segmented into their figure and ground regions in an automatic learning process. This is in contrast with previous approaches, which required complete manual segmentation of the objects in the training examples. The figure-ground learning combines top-down and bottom-up processes and proceeds in two stages, an initial approximation followed by iterative refinement. The initial approximation produces figure-ground labeling of individual image fragments using the unsegmented training images. It is based on the fact that on average, points inside the object are covered by more fragments than points outside it. The initial labeling is then improved by an iterative refinement process, which converges in up to three steps. At each step, the figure-ground labeling of individual fragments produces a segmentation of complete objects in the training images, which in turn induce a refined figure-ground labeling of the individual fragments. In this manner, we obtain a scheme that starts from unsegmented training images, learns the figure-ground labeling of image fragments, and then uses this labeling to segment novel images. Our experiments demonstrate that the learned segmentation achieves the same level of accuracy as methods using manual segmentation of training images, producing an automatic and robust top-down segmentation.

In this work we show how to combine bottom-up and top-down approaches into a single figure-ground segmentation process. This process provides accurate delineation of object boundaries that cannot be achieved by either the top-down or bottom-up approach alone. The top-down approach uses object representation learned from examples to detect an object in a given input image and provide an approximation to its figure-ground segmentation. The bottom-up approach uses image-based criteria to define coherent groups of pixels that are likely to belong together to either the figure or the background part. The combination provides a final segmentation that draws on the relative merits of both approaches: The result is as close as possible to the top-down approximation, but is also constrained by the bottom-up process to be consistent with significant image discontinuities. We construct a global cost function that represents these top-down and bottom-up requirements. We then show how the global minimum of this function can be efficiently found by applying the sum-product algorithm. This algorithm also provides a confidence map that can be used to identify image regions where additional top-down or bottom-up information may further improve the segmentation. Our experiments show that the results derived from the algorithm are superior to results given by a pure top-down or pure bottom-up approach. The scheme has broad applicability, enabling the combined use of a range of existing bottom-up and top-down segmentations.

In this paper we show that efpoundcient object recognition can be obtained by combining informative features with linear classipoundcation. The results demonstrate the superiority of informative class-specipoundc features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear separation rules, while generic, feature based classipounders require more complex-classipoundcation schemes. This is signipoundcant because efpoundcient and optimal methods have been developed for spaces that allow linear separation. To compare different strategies for feature extraction, we trained and compared classipounders working in feature spaces of the same low dimensionality, using two feature types (image fragments vs. wavelets) and two classipoundcation rules (linear hyperplane and a Bayesian Network). The results show that by maximizing the individual information of the features, it is possible to obtain efpoundcient classipoundcation by a simple linear separating rule, as well as more efpoundcient learning.

In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined.

A method is presented for class-based recognition using a small number of example views taken under several different viewing conditions. The main emphasis is on using a small number of examples. Previous work assumed that the set of examples is sufficient to span the entire space of possible objects, and that in generalizing to a new viewing conditions a sufficient number of previous examples under the new conditions will be available to the recognition system. Here we have considerably relaxed these assumptions and consequently obtained good class-based generalization from a small number of examples, even a single example view, for both viewing position and illumination changes. In addition, previous class-based approaches only focused on viewing position changes and did not deal with illumination changes. Here we used a class-based approach that can generalize for both illumination and viewing position changes. The method was applied to face and car model images. New views under viewing position and illumination changes were synthesized from a small number of examples.

We describe an approach to object recognition in which the image-to-model match is based on stochastic optimization. During the recognition process, an internal model is matched with a novel object view. To compensate for changes in viewing conditions (such as illumination, viewing direction), the model is controlled by a number of parameters. The matching is obtained by seeking a setting of the parameters that minimizes the discrepancy, between the image and the model. The search is performed in our examples in a six-dimensional space with multiple local minima. We developed an efficient minimization method based on the stochastic optimization approach (Mockus 1989). The search is bidirectional (applied to both the model and the image) and avoids the difficult problem of establishing image-to-model correspondence. It proceeds by evolving a population of candidate solutions using simple generation rules, based on the autocorrelation of the search space. We describe the method, its application to objects in several domains (cars, faces, printed symbols), and experimental comparisons with alternative methods, such as simulated annealing.

In recognizing objects and scenes, partial recognition of objects or their parts can be used to guide the recognition of other objects. Here, the role of individual objects in the recognition of complete figures and the influence of contextual information on the identification of ambiguous objects were investigated. Configurations of objects that were placed in either proper or improper spatial relations were used, and response times and error rates in a recognition task were measured. Two main results were obtained. First, proper spatial relations among the objects of a scene decrease response times and error rates in the recognition of individual objects. Second, the presence of objects that have a unique interpretation improves the identification of ambigous objects in the scene. Ambiguous objects were recognized faster and with fewer errors in the presence of clearly recognized objects compared with the same objects in isolation or in improper spatial relations. The implications of these findings for the organization of recognition memory are discussed.

This paper discusses two problems related to three-dimensional object recognition. The first is segmentation and the selection of a candidate object in the image, the second is the recognition of a three-dimensional object from different viewing positions. Regarding segmentation, it is shown how globally salient structures can be extracted from a contour image based on geometrical attributes, including smoothness and contour length. This computation is performed by a parallel network of locally connected neuron-like elements. With respect to the effect of viewing, it is shown how the problem can be overcome by using the linear combinations of a small number of two-dimensional object views. In both problems the emphasis is on methods that are relatively low level in nature. Segmentation is performed using a bottom-up process, driven by the geometry of image contours. Recognition is performed without using explicit three-dimensional models, but by the direct manipulation of two-dimensional images.

A new method for computing the direction of translational motion (focus of expansion) of a moving observer (or camera) in a stationary environment is proposed. The method applies simple 1-D search directly to velocity field measurements of the changing image, for cases of general motion and constrained motion. In the case of general motion, the velocity field is derotated to cancel the velocity of the observed point at the image origin. In principle, a single moving closed contour which encircles the origin is sufficient to recover the focus of expansion. In practice, additional velocity measurements in the image may be incorporated in the computation, for improved robustness in the face of image noise and velocity field inaccuracies.

The human visual system can make remarkably precise spatial judgements. There are reasons to believe that this accuracy is achieved and maintained by using processes that calibrate and correct errors in the system. This work investigate this problem of self-calibration and describes an adaptive system for detecting the collinearity of points and the straightness of lines. The system is initially inaccurate, but, by using an error correction mechanism, it eventually becomes highly accurate. The error correction is performed by a simple self calibration process named proportional multi-gain adjustment. The calibration process adjusts the gain values of the system input units. The process utilizes statistical regularities in the input stimuli. It compensate for errors due to noise in the input units receptive fields location and response functions by ensuring that the average deviation from collinearity offset detected by the system is zero. As a by product of the error correction, the system exhibits also adaptation and aftereffect phenomena, similar to those observed in the human visual system.

A theory of early visual information processing proposed by Marr and co-workers suggests that simple cortical cells may be involved in the detection of zero crossing in the retinal output. We have tested this theory by using pairs of adjacent edges (staircases stimuli) and recording from edge-specific simple cells in cat striate cortex. The zero crossing hypothesis gives rise for such stimuli to non-obvious predictions that were generally confirmed by the experiment.

Light-source detection as a result of many cooperative computations in human vision is discussed. A method is presented for accomplishing light source detection in the so- called Mondrian world. The method involves a comparison of changes in intensity and changes in the derivative of intensity at region boundaries.