Recognising human activities from sensors embedded in an environment or worn on bodies is an important and challenging research topic in pervasive computing. Existing work on activity recognition is mainly concerned with identifying single user sequential activities from well-scripted or pre-segmented sequences of sensor events. However a real-world environment often contains multiple users, wi...

in the analysis of spatial point patterns, complete spatial randomness (csr) hypothesis,which is a restriction of a homogenous poisson process to study region a, operates as a dividinghypothesis between “regular” and “aggregated” patterns. meanwhile, many alternatives to csr inaggregated patterns are extensions of homogenous poisson processes themselves. therefore, when thecsr hypothesis is rej...

In the complex marine environment, target recognition is difficult, and real-time detection has a slow speed. this article, method combining underwater generative adversarial network improved YOLOv4 proposed, which named M-YOLOv4. Firstly, images collected by inspection robot are enhanced using algorithm to obtain training datasets. Secondly, combines feature extraction of MoblieNetv3 for light...

Historically, researchers in the field have spent a great deal of effort to create image representations that have scale invariance and retain spatial location information. This paper proposes to encode equivalent temporal characteristics in video representations for action recognition. To achieve temporal scale invariance, we develop a method called temporal scale pyramid (TSP). To encode temp...

5th International Symposium on Visual Computing, Las Vegas, Nevada, USA, Nov 30 Dec 2, 2009 The Laplacian pyramid recursively splits an image into local averages and local differences using a fixed Gaussian interpolation function. We propose a spatially variant interpolation function that is adaptive to curvilinear edges in the image. Unlike the signal-based multiscale analysis where a step edg...

Thi s paper presents a new method for multiscale segmentation of volume images. The segmentation i s achieved using a recent nonlinear transform which leads to well-characterized regions at dif ferent spatial and intens i ty scales. T h e detected three-dimensional regions are closed and are homogeneous relative to their surround. A pyramid is generated containing the region information extract...

In this paper, we propose a new formulation of the Differential Optical Flow Equation (DOFE) between two consecutive images considering spatial and temporal information from both. The displacement field is computed in a Markov Random Field (MRF) framework. The solution is done by minimization of the Gibbs energy using a Direct Descent Energy (DDE) algorithm. A hybrid multiresolution approach, c...

In recent years, rapidly developed hand written word recognition techniques have attracted researcher’s attention to study Arabic word classification. Arabic language has cursive style of writing so it needs special framework for classification. In this paper, a precise framework for Arabic word classification is presented, which uses sparse coding with spatial pyramid matching (SPM) algorithm ...

We present a method for visual object classification using only a single feature, transformed color SIFT [15] with a variant of Spatial Pyramid Matching (SPM) that we called Sliding Spatial Pyramid Matching (SSPM), trained with an ensemble of linear regression (provided by LINEAR) to obtained state of the art result on Caltech-101 [22] of 83.46%. SSPM is a special version of SPM where instead o...

Recently spatial pyramid matching (SPM) with scale invariant feature transform (SIFT) descriptor has been successfully used in image classification. Unfortunately, the codebook generation and feature quantization procedures using SIFT feature have the high complexity both in time and space. To address this problem, in this paper, we propose an approach which combines local binary patterns (LBP)...