Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

ALGORITHM AND METHODS OF RANKING GROUP OF BITMAP IMAGES

The article presents an algorithm and a methodology of ranking a group of raster images by using the criterion of their expected quality. Ranking refers to the evaluation of a sample of bitmap images in a descending order of their quality, the image quality assessment being performed on the basis of a number of statistical parameters, such as coefficients of variation, determination, rank correlation index, as well as errors (absolute maximum error, average error, average quadratic error). The differences between the images are based on converting a full-color RGB image into HSV, Lab, NTSC, XYZ, YCbCr color models, which are represented as one-dimensional pixel ma-trices. The colour model RGB is taken as a reference. In relation to it, the proposed statistical char-acteristics of other color models are compared, any object of each color model being compared with the base model - an RGB image. Based on this comparison, all images of a given group are analyzed independently of each other. Image quality assessment is performed in a module that can be used to cycle through multiple images and is represented in numerical form as a real number. One of the module blocks calculates the statistical parameters between each color model and the base RGB model. After receiving the values of the quality scores they are ranked according to their values. As a result, an image with a higher or lower scene quality can be determined. Images with blocking artifacts, noisy images of the salt & pepper type, and images with strobe effects artifacts were considered as test images.

High Capacity Reversible Steganography on CMY and HSI Color Images Using Image Interpolation

Steganography using image interpolation has created a new research area in multimedia communication. A reversible data concealing in HSI and CMY color models using image interpolation is proposed in this paper. The HSI and CMY image models are interpolated using High Capacity Reversible Steganography (CRS) technique. The median plane of both HSI and CMY color models are selected for secret message bit concealing. The secret message bits are concealed in the cover plane by Exclusive OR (XOR) operation. Since the cover image is recovered after secret message bit retrieval, this finds application in military and medical imaging applications. The experimental results of proposed scheme showed very high embedding capacity of about 16 bits in each pixel location of calculated pixel value, good image quality with a surface similarity index measure (SSIM) value 1 and high PSNR. Also, high robustness is achieved on comparing with the existing works.

Histogram Adjustment of Images for Improving Photogrammetric Reconstruction

The accuracy of photogrammetric reconstruction depends largely on the acquisition conditions and on the quality of input photographs. This paper proposes methods of improving raster images that increase photogrammetric reconstruction accuracy. These methods are based on modifying color image histograms. Special emphasis was placed on the selection of channels of the RGB and CIE L*a*b* color models for further improvement of the reconstruction process. A methodology was proposed for assessing the quality of reconstruction based on premade reference models using positional statistics. The analysis of the influence of image enhancement on reconstruction was carried out for various types of objects. The proposed methods can significantly improve the quality of reconstruction. The superiority of methods based on the luminance channel of the L*a*b* model was demonstrated. Our studies indicated high efficiency of the histogram equalization method (HE), although these results were not highly distinctive for all performed tests.

Investigation into the predictive performance of colorimetric sensor strips using RGB, CMYK, HSV, and CIELAB coupled with various data preprocessing methods: a case study on an analysis of water quality parameters

The potential use of colorimetric sensors has received significant attention due to its feasibility for use in various applications. After reacting with a sample, the image of the colorimetric sensor can be captured and converted into digital data using several different color models. The analytical data can then be processed with various chemometric methods. This research study investigated the predictive performance of calibration models established using color models commonly used in analytical chemistry including RGB, CMYK, HSV and CIELAB. A total of eight commercially available colorimetric sensors were used to determine the presence of manganese (Mn2+), copper (Cu2+), iron (Fe2+/Fe3+), nitrate (NO3–), phosphate (PO43–), sulfate (SO42–), as well as total hardness and pH values. As external validation tests, real water samples collected in Chiang Mai, Thailand were used. Based on the resulting data obtained using the synthetic test samples, the color that was most similar to the appearing color of the chemical sensor could offer satisfactory results. However, it was not always the case especially when the strips composed of multiple colorimetric sensors or sensor array were used. When tested with external validation, the predictive performance could be improved using appropriate data preprocessing and, in this research study, a normalization method was recommended to guarantee the accuracy of the calibration models.

Compact Deep Color Features for Remote Sensing Scene Classification

Aerial scene classification is a challenging problem in understanding high-resolution remote sensing images. Most recent aerial scene classification approaches are based on Convolutional Neural Networks (CNNs). These CNN models are trained on a large amount of labeled data and the de facto practice is to use RGB patches as input to the networks. However, the importance of color within the deep learning framework is yet to be investigated for aerial scene classification. In this work, we investigate the fusion of several deep color models, trained using color representations, for aerial scene classification. We show that combining several deep color models significantly improves the recognition performance compared to using the RGB network alone. This improvement in classification performance is, however, achieved at the cost of a high-dimensional final image representation. We propose to use an information theoretic compression approach to counter this issue, leading to a compact deep color feature set without any significant loss in accuracy. Comprehensive experiments are performed on five remote sensing scene classification benchmarks: UC-Merced with 21 scene classes, WHU-RS19 with 19 scene types, RSSCN7 with 7 categories, AID with 30 aerial scene classes, and NWPU-RESISC45 with 45 categories. Our results clearly demonstrate that the fusion of deep color features always improves the overall classification performance compared to the standard RGB deep features. On the large-scale NWPU-RESISC45 dataset, our deep color features provide a significant absolute gain of 4.3% over the standard RGB deep features.