Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.

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WLD: A Robust Local Image Descriptor

Texture features for image classification and retrieval.

From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs.-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.

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POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

The purpose of this paper is to present the most important achievements in the field of distribution power line inspection by mobile robots. Stimulated by the need for fast, accurate, safe and low-cost power line inspection, which would increase the quality of power delivery, the field of automated power line inspection has witnessed rapid development over the last decade. This paper addresses automated helicopter inspection, inspection with flying robots and inspection with climbing robots. The first attempts to automate power line inspection were conducted in the field of helicopter inspection. In recent years, however, the research was mostly focused on flying and climbing robots. These two types of robots for automated power line inspection are critically assessed according to four important characteristics: design requirements, inspection quality, autonomy and universality of inspection. Besides, some general not yet identified problems and tasks of inspection robots, which should be addressed in the future, are presented. In conclusion, the two robot types have specific benefits and drawbacks so that none can currently be considered generally advantageous.

A Survey of Mobile Robots for Distribution Power Line Inspection

advances in kernel methods support vector learning is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the advances in kernel methods support vector learning is universally compatible with any devices to read.

Advances In Kernel Methods Support Vector Learning

A way to reduce the falling risk in the area where closed-circuit television (CCTV) is not available has been studied systematically using ultrasonic sensors for detection. The ultrasonic sensors, behaving a receiver and a transmitter, connect to Arduino microcontroller in order to send the signal via WiFi to computer. The sensors are positioned to array on top and wall of room. Then, the threshold signal is analyzed to find the action such as stand, sit and fall by comparison between top and side signal. The various side signals can indicate the failure. In addition, the signal processing can achieve the temperature around the simple leading to classify an object or a human. The accuracy to indicate the fall is 92 percentages.

Ultrasonic array sensors for monitoring of human fall detection

Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses. This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such as k-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases. Performance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods. An EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses.

/pdf/an-emg-based-feature-extraction-method-using-a-normalized-4tj67mivsg.pdf

An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection

Particle filtering with adaptive resampling scheme for modal frequency identification and dispersion curves estimation in ocean acoustics

The reporting causes of death in Thailand. The cancer is most common cause of death in Thailand, including colon cancer, lung cancer, breast cancer, cervical cancer, etc. The skin cancer is one of cancers that increase every year. Considering and analyzing features of cancer image, which includes Asymmetry, Border Irregularity, Compact Index, Fractal Dimension, Edge Abruptness, Color variation and Diameter, is popular technique of analysis the patients with skin cancer. To extract and analyze such features, image segmentation plays important role for automatic skin cancer detection system. In this paper, we propose the image segmentation scheme based on Support Vector Machine (SVM) and Snake active contour. SVM is used to help finding the appropriate parameters for snake algorithm.

Kosin Chamnongthai

Papers

Ultrasonic array sensors for monitoring of human fall detection

An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection

Particle filtering with adaptive resampling scheme for modal frequency identification and dispersion curves estimation in ocean acoustics

Detection skin cancer using SVM and snake model

Signer-independence finger alphabet recognition using discrete wavelet transform and area level run lengths