Multivariate analysis of images in spectrophotometric methods: Quantification of soil organic matter

Soil organic matter (SOM) is usually quantified by Walkley-Black titration method or using a spectrophotometric method. This study proposes an alternative method for quantification of SOM using digital image from scanner and mathematical algorithms to replace titration and spectrophotometry procedures. For this, after SOM oxidation by potassium dichromate, digital images were acquired. Posteriorly, extraction of RGB color histograms from images have occurred, followed by the use of multivariate calibration method: partial least squares (PLS). Six soil samples were analyzed. We used the Walkley-Black method as reference. SOM was estimated by images using the PLS tool. The new method, besides being a fast, low cost, and more operational alternative, presented statistically equal results in relation to the reference method, as assessed by the Student t-test and F-test at 95 % confidence.

Time Classification Algorithm Based on Windowed-Color Histogram Matching

A web-based search system recommends and gives results such as customized image or video contents using information such as user interests, search time, and place. Time information extracted from images can be used as a important metadata in the web search system. We present an efficient algorithm to classify time period into day, dawn, and night when the input is a single image with a sky region. We employ the Mask R-CNN to extract a sky region. Based on the extracted sky region, reference color histograms are generated, which can be considered as the ground-truth. To compare the histograms effectively, we design the windowed-color histograms (for RGB bands) to compare each time period from the sky region of the reference data with one of the input images. Also, we use a weighting approach to reflect a more separable feature on the windowed-color histogram. With the proposed windowed-color histogram, we verify about 91% of the recognition accuracy in the test data. Compared with the existing deep neural network models, we verify that the proposed algorithm achieves better performance in the test dataset.

Effect of Various Forms of Aluminum 6082 on the Mechanical Properties, Microstructure and Surface Modification of the Profile after Extrusion Process

This article presents a method of reusing aluminum scrap from alloy 6082 using the hot extrusion process. Aluminum chips from milling and turning processes, having different sizes and morphologies, were cold pressed into briquettes prior to hot pressing at 400 °C at a ram speed of 2 mm/s. The study of mechanical properties combined with observations of the microstructures, as well as tests of density, hardness and electrical conductivity were carried out. On the basis of the results, the possibility of using the plastic consolidation method and obtaining materials with similar to a solid ingot mechanical properties, density and electrical conductivity was proven. The possibility of modifying the surface of consolidated aluminum scrap was tested in processes examples: polishing, anodizing and coloring. For this purpose, a number of analyses and tests were carried out: comparison of colors on color histograms, roughness determination, SEM and chemical composition analysis. It has been proven there are differences in the surface treatment of the solid material and that of scrap consolidation, and as such, these differences may significantly affect the final quality.

An effective shape descriptor for content based image retrieval

In this work, we present a modified version of the Generic Fourier Descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduced the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise tend to contaminate the shape feature description. We find that the proposed techniques not only improve overall retrieval in terms of shape, but more importantly, provide a more accurate similarity ranking measure of retrieved results, demonstrating the need for greater consideration for dominant internal and external shape details. A feature database combined by color moments, color histograms, Gabor wavelet and shape features is applied in our image retrieval system. Relevance feedback has also been considered, bridging the gap between the high level concepts and the low level visual features. The experimental results indicate that dynamically updating weights associated with feature compenents by users' feedback greatly improves retrieval performance.

An effective shape descriptor for content based image retrieval

In this work, we present a modified version of the Generic Fourier Descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduced the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise tend to contaminate the shape feature description. We find that the proposed techniques not only improve overall retrieval in terms of shape, but more importantly, provide a more accurate similarity ranking measure of retrieved results, demonstrating the need for greater consideration for dominant internal and external shape details. A feature database combined by color moments, color histograms, Gabor wavelet and shape features is applied in our image retrieval system. Relevance feedback has also been considered, bridging the gap between the high level concepts and the low level visual features. The experimental results indicate that dynamically updating weights associated with feature compenents by users' feedback greatly improves retrieval performance.

Shot Boundary Detection Method Based on a New Extensive Dataset and Mixed Features

Shot boundary detection in video is one of the key stages of video data processing. A new method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed. The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2] datasets, and the MSU CC datasets, based on videos used in the video codec comparison conducted at MSU, as well as videos from the IBM set, were also plotted. The total dataset for algorithm development and testing exceeded the known TRECVID datasets. Based on the test results, the proposed algorithm for scene change detection outperformed its counterparts with a final F-score of 0.9794.

Real-Time Object Tracking via Adaptive Correlation Filters

Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.

State Pod Automatism as Art Therapy.

Formal art therapy is defined in a definition of the formal elements in art and metrics on structure, contour, variation and balance, leading to adaptability and measures of dynamism and thought as an emergence in the art form, leading to a viable diagnosis. In this paper we examine formal art analysis in art therapy and describe several metrics based on common data mining algorithms.Keywords: Formal Art Therapy, Autopilot, Mindfulness, Wilderness Therapy, structure, variation, segmentation, objects in images, dynamism, balance, color histograms, k means, data mining. What:In understanding the Autopilot, we use art therapy in the form of the state pod automatism,in this coding , the formal elements consist of a flexible line composition with five or more anchor points to create pods, which are painted in a spectrum of colors using a pixel brush, either as oil or water colors, in as many colors and hues as possible, the transitions between the pods, represent the nature of the inertial, the reason for dukka.(Contributors to Wikimedia projects 2001)We present data mining algorithms for structure and color, and contour based metrics for assisting a prognosis and art therapy formulation.How:Three examples are presented and analyzed using k-means algorithms to create statistics for colors, color clusters and pixels, with a database lookup of matching words as a meta description of the images. We present the analysis, and propose contour and structure mining tools for generating metrics.(Contributors to Wikimedia projects 2005)