3,857 research outputs found
Gravity optimised particle filter for hand tracking
This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles
Object Detection and Tracking using Watershed Segmentation and KLT Tracker
In this paper a moving object is extracted from a video using video object detection algorithm based on spatial and temporal segmentation The technique begins with temporal segmentation in which edge map is extracted using edge operator The initial binary mask is obtained by using morphological operation applied on initial edge map The next phase is spatial segmentation where gradient image is obtained by multi-scale morphological operator The modified gradient image is obtained by the operator applied over the current frame At last moving object is extracted by precisely and accurately by watershed segmentation which is performed on the modified gradient image Again morphological operation is applied on the output to get final binary mask This binary mask is then complemented to yield the contour line of the video object Using the binary mask the video object is extracted from the video frames After detection of video object the object tracking is performed using Kanade Lucas Tomasi KLT feature tracke
An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis
This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
Sophisticated machine learning techniques have promising potential in search
for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional
neural networks (CNN) can provide powerful tools for differentiating between
patterns of calorimeter energy deposits by prompt particles of Standard Model
and long-lived particles predicted in various models beyond the Standard Model.
We demonstrate the usefulness of CNN by using a couple of physics examples from
well motivated BSM scenarios predicting long-lived particles giving rise to
displaced jets. Our work suggests that modern machine-learning techniques have
potential to discriminate between energy deposition patterns of prompt and
long-lived particles, and thus, they can be useful tools in such searches.Comment: 32 pages, 17 figures; version accepted for publication in JHE
Detecting outlying subspaces for high-dimensional data: the new task, algorithms and performance
[Abstract]: In this paper, we identify a new task for studying the outlying degree (OD) of high-dimensional data, i.e. finding the subspaces (subsets of features)
in which the given points are outliers, which are called their outlying subspaces. Since the state-of-the-art outlier detection techniques fail to handle this
new problem, we propose a novel detection algorithm, called High-Dimension Outlying subspace Detection (HighDOD), to detect the outlying subspaces of
high-dimensional data efficiently. The intuitive idea of HighDOD is that we measure the OD of the point using the sum of distances between this point and its k nearest neighbors. Two heuristic pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms other searching alternatives such as the naive topādown, bottomāup and random search methods, and the existing
outlier detection methods cannot fulfill this new task effectively
AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy
Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the
third most commonly diagnosed cancer in males and the second in females. For
colorectal cancer, the best screening test available is the colonoscopy. During
a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a
video of the internal mucosa of the colon. The video data are displayed on a
monitor for the physician to examine the lining of the entire colon and check
for colorectal polyps. Detection and removal of colorectal polyps are
associated with a reduction in mortality from colorectal cancer. However, the
miss rate of polyp detection during colonoscopy procedure is often high even
for very experienced physicians. The reason lies in the high variation of polyp
in terms of shape, size, textural, color and illumination. Though challenging,
with the great advances in object detection techniques, automated polyp
detection still demonstrates a great potential in reducing the false negative
rate while maintaining a high precision. In this paper, we propose a novel
anchor free polyp detector that can localize polyps without using predefined
anchor boxes. To further strengthen the model, we leverage a Context
Enhancement Module and Cosine Ground truth Projection. Our approach can respond
in real time while achieving state-of-the-art performance with 99.36% precision
and 96.44% recall
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