Image classification is an important topic in computer vision. As a key procedure, encoding the local features to get a compact representation for image affects the final classification accuracy largely. There is no doubt that encoding procedure leads to information loss, due to the existence of quantization error. The residual vector, defined as the difference between the local image feature a...

When a different pattern is presented to each eye, the perceived image spontaneously alternates between the two patterns (binocular rivalry); the dynamics of these bistable alternations are known to be stochastic. Examining multistable binocular rivalry (involving four dominant percepts), we demonstrated path dependence and on-line adaptation, which were equivalent whether perceived patterns we...

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...

Visual recognition of patterns is a basic capability of most advanced species in nature. A big percentage of the human brain is devoted to visual processing with a substantial portion of this area used for pattern recognition. Visual pattern recognition enables a variety of tasks such as object and target recognition, navigation, grasping and manipulation, among others. Advances in camera techn...

We propose a method for coding of segmented images based on shape adaptive wavelet transform approach. Wavelet transform is computed directly on particular image segments without extension outside the boundaries. We adapted the transform method from [1], where it was used for compression of image segments but less effective zero tree coding was involved. Though used approach does not yet outper...

In a previous work [12, 11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten ...

1 FAST NOISE SUPPRESSION FOR LOSSLESS IMAGE CODING (EXTENDED PREPRINT) Tilo Strutz University of Rostock, Institute of Communications and Information Electronics Richard-Wagner-Str.31, 18119 Rostock, FRG ABSTRACT: This contribution presents a new denoising method for applications requiring preservation of highest visual image quality. The aim of the new approach is not to suppress all noise ins...

The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Object recognition has been effectively performed by aggregating sparse codes of local features in an image at multiple spatial scales. Though sparse coding guarantees a highfidelity representation, it does not exploit the dependence between the local features. By incorpor...

For visual grading experiments, which are an easy and increasingly popular way of studying image quality, hitherto used data analysis methods are often inadequate. Visual grading analysis makes assumptions that are not statistically appropriate for ordinal data, and visual grading characteristic curves are difficult to apply in more complex experimental designs. The approach proposed in this pa...