The relation between color spaces and compositional data analysis demonstrated with magnetic resonance image processing applications

This paper presents a novel application of compositional data analysis methods in the context of color image processing. A vector decomposition method is proposed to reveal compositional components of any vector with positive components followed by compositional data analysis to demonstrate the relation between color space concepts such as hue and saturation to their compositional counterparts. The proposed methods are applied to a magnetic resonance imaging dataset acquired from a living human brain and a digital color photograph to perform image fusion. Potential future applications in magnetic resonance imaging are mentioned and the benefits/disadvantages of the proposed methods are discussed in terms of color image processing.Comment: 13 pages, 3 figures, short paper, submitted to Austrian Journal of Statistics compositional data analysis special issue, first revision, fix rendering error in fig

A Matrix Based Approach for Color Transformations in Reflections

In this thesis, I demonstrate the feasibility of linear regression with 4 × 4 matrices to perform color transformations, specifically looking at the case of color transformations in reflections. I compare and analyze the power and performance linear regression models based on 3 × 3 and 4 × 4 matrices. I conclude that using 4 × 4 matrices in linear regression is more advantageous in power and performance over using 3 × 3 matrices in linear regressions, as 4 × 4 matrices allow for categorically more transformations by including the possibility of translation. This provides more general affine transformations to a color space, rather than being restricted to passing through the origin. I examine the benefits of allowing for negative elements in color transformation matrices. I also touch on the possible differences in application between filled 4 × 4 matrices and diagonal 4 × 4 matrices, and discuss the limitations inherent to linear regression used in any type of matrix operations

Rancang Bangun Deteksi Objek dengan Metode Filter Warna HSV pada Sistem Klasifikasi Kualitas Biji Kopi Berbasis NVIDIA Jetson Nano

The Post-harvest coffee bean selection plays a crucial role in ensuring optimal bean quality during production processes. Currently, this process is manually conducted in Indonesia. Implementing computer vision can enhance the objectivity of the sorting process through machine vision. An effective object detection system is essential to support a prototype coffee bean quality classification system based on NVIDIA Jetson Nano. The Hue Saturation Value (HSV) color filter method proves effective in detecting objects within a given image frame. Performance evaluation is conducted by assessing the alignment between workflow design and system operation. While the webcam-based object detection system successfully deployed, its effectively identifies coffee bean objects, it faces limitations in detecting smaller, dark-colored beans beyond the specified HSV color threshold. These limitations are attributed to the webcam's specifications, including its rolling shutter, which results in a 'jello effect' when dealing with moving objects

Background Subtraction for Night Videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training

What can Associative Learning do for Driving?

This is the final version of the paper. Available from Cognitive Science Society via the link in this record.To improve road safety, it is important to understand the impact that the contingencies around traffic lights have upon drivers’ behavior. There are formal rules that govern behavior at UK traffic lights (see The Highway Code, 2015), but what does experience of the contingencies do to us? While a green light always cues a go response and a singleton red a stop, the behavior linked to amber is ambiguous; in the presence of red it cues readiness to start, while on its own it cues "preparation" to stop. Could it be that the contingencies between stimuli and responses lead to implicit learning of responses that differ from those suggested by the rules of the road? This study used an incidental go/no-go task in which colored shapes were stochastically predictive of whether a response was required. The stimuli encoded the contingencies between traffic lights and their appropriate responses, for example, stimulus G was a go cue, mimicking the response to a green light. Evidence was found to indicate that G was a go cue, while A (which had the same contingencies as an amber light) was a weak go cue, and that R (a stop cue) was surprisingly responded to as a neutral cue.W.G.N. is supported by an ERSC studentship (ES/J50015X/1)