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An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and a...

"The retrosplenial cortex (RSC) has been widely related to spatial and contextual memory. However, we recently demonstrated that the anterior part of the RSC (aRSC) is required for object recognition (OR) memory consolid...

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on ...

Subjects / keywords: Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Object (grammar); Software; Set (abstract data type); Cognitive neuroscience of visual object recognition; Precision and recall; ...

"Recognition memory can rely on three components: “what”, “where” and “when”. Recently we demonstrated that the anterior retrosplenial cortex (aRSC), like the perirhinal cortex (PRH) and unlike the hippocampu...

Subjects / keywords: Computer science; Fuzzy logic; Algorithm; Function (biology); Pattern recognition (psychology); Artificial intelligence; Biology; Evolutionary biology

The development of parallel algorithms for an automatic recognition and classification of objects from an industrial line (either production or packaging) is presented. This kind of problem introduces a temporal restrict...

Over the past decades, an increasingly influential Political Philosophy approach has been seen to defend issues relating to cultural injustices. The daily struggles arising from political agendas within different societi...

"Spaced training, which involves long inter-trial intervals, has positive effects on memories. One of the main attributes of long-term memories (LTM) is persistence. Here, to identify the process that promotes LTM persis...

The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylind...

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among...

Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a ve...

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In...

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally t...

The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such ...

Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetabl...

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disci...

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers...

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional netwo...

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrar...

We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptor...

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most in...

The image recognizing process requires the identification of every logical object that compose every image, which first implies to recognize it as an object (segmentation) and then identify which object is, or at least w...

In the Laboratory of Research and Development on Computer Science of the National University of La Plata, a face recognition system has been developed. This article describes a series of testings based on parallel proces...