A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.
Two low-cost methods of estimating the road surface condition are presented in the paper, the first one based on the use of accelerometers and the other on the analysis of images acquired from cameras installed in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver and a multi-axis accelerometer. The measurement data were collected from recorded ride sessions taken place on diversified road surface roughness conditions and at varied vehicle speeds on each of examined road sections. The data were gathered for various vehicle body types and afterwards successful attempts were made in constructing the road surface classification employing the created algorithm. In turn, in the video method, a set of algorithms processing images from a depth camera and RGB cameras were created. A representative sample of the material to be analysed was obtained and a neural network model for classification of road defects was trained. The research has shown high effectiveness of applying the digital image processing to rejection of images of undamaged surface, exceeding 80%. Average effectiveness of identification of road defects amounted to 70%. The paper presents the methods of collecting and processing the data related to surface damage as well as the results of analyses and conclusions.
Image processing techniques (band rationing, color composite, Principal Component Analyses) are widely used by many researchers to describe various mines and minerals. The primary aim of this study is to use remote sensing data to identify iron deposits and gossans located in Kaman, Kırşehir region in the central part of Anatolia, Turkey. Capability of image processing techniques is proved to be highly useful to detect iron and gossan zones. Landsat ETM+ was used to create remote sensing images with the purpose of enhancing iron and gossan detection by applying ArcMap image processing techniques. The methods used for mapping iron and gossan area are 3/1 band rationing, 3/5 : 1/3 : 5/7 color composite, third PC and PC4 : PC3 : PC2 as RG B which obtained result from Standard Principal Component Analysis and third PC which obtained result from Developed Selected Principal Component Analyses (Crosta Technique), respectively. Iron-rich or gossan zones were mapped through classification technique applied to obtained images. Iron and gossan content maps were designed as final products. These data were confirmed by field observations. It was observed that iron rich and gossan zones could be detected through remote sensing techniques to a great extent. This study shows that remote sensing techniques offer significant advantages to detect iron rich and gossan zones. It is necessary to confirm the iron deposites and gossan zones that have been detected for the time being through field observations.
Based on the mould temperature measured by thermocouples during slab continuous casting, a difference of temperature thermograph is developed to detect slab cracks. In order to detect abnormal temperature region caused by longitudinal crack, the suspicious regions are extracted and divided by virtue of computer image processing algorithms, such as threshold segmentation, connected region judgement and boundary tracing. The abnormal regions are then determined and labeled with the eight connected component labeling algorithm. The boundary of abnormal region is also extracted to depict characteristics of longitudinal crack. Based on above researches, longitudinal crack with abnormal temperature region can be detected and is different from other abnormalities. Four samples of temperature drop are picked up to compare with longitudinal crack on the abnormal region formation, length, width, shape, et al. The results show that the abnormal region caused by longitudinal crack has a linear and vertical shape. The height of abnormal region is more than the width obviously. The ratio of height to width is usually larger than that of other temperature drop regions. This method provides a visual and easy way to detect longitudinal crack and other abnormities. Meanwhile it has a positive meaning to the intelligent and visual mould monitoring system of continuous casting.
Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with a single neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.