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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...

Materias / palabras clave: Artificial intelligence; Pattern recognition (psychology); Clutter; Affine transformation; Computer science; Cognitive neuroscience of visual object recognition; Computer vision; Invariant (physics); Hough transform; 3D ...

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...

We present the automated techniques we have developed for new software that optimally detects, deblends, measures and classifies sources from astronomical images: SExtractor (Source Extractor ). We show that a very relia...

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...

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...

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinatio...

Multiscale feature extraction from the visual environment in an active vision system Y.Machrouh 1, J.-S.Liénard 1, P.Tarroux 1, 2 Abstract. This paper presents a visual architecture able to identify salient regions in a...