A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances

Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k−means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.

Application Notes - APD LAMP Diagnostic App: Automated Colorimetric Analysis and Documentation

Amidst rapid diagnostic kits, reverse transcription loop-mediated isothermal amplification (RT-LAMP) has emerged as a rapid point-of-care testing (POCT) method during the COVID-19 pandemic. While many POCT kits rely on plate readers or visual classifications, these processes require experienced staff, and with the use of plate readers, the need for peripheral equipment and infrastructure as well. To address the gap and ensure objectivity in colorimetric POCT kits, the Automated Product Determination (APD) LAMP Diagnostic App was developed for automatic colorimetric analysis of single to multiple LAMP samples. Leveraging on the smartphone camera, barcode-based documentation feature, and a colour distance formula, the app algorithm calculates RGB values, labelling samples as “positive” when yellow, “negative” when pink, and “unknown” when orange. The APD Lamp diagnostic app for Android hereby demonstrates the integration of smartphone apps in POCT kits and ways the smartphone revolution changes laboratory processes to be timely and on-the-go.

Does conspicuousness scale linearly with colour distance? A test using reef fish

To be effective, animal colour signals must attract attention—and therefore need to be conspicuous. To understand the signal function, it is useful to evaluate their conspicuousness to relevant viewers under various environmental conditions, including when visual scenes are cluttered by objects of varying colour. A widely used metric of colour difference (Δ S ) is based on the receptor noise limited (RNL) model, which was originally proposed to determine when two similar colours appear different from one another, termed the discrimination threshold (or just noticeable difference). Estimates of the perceptual distances between colours that exceed this threshold—termed ‘suprathreshold’ colour differences—often assume that a colour's conspicuousness scales linearly with colour distance, and that this scale is independent of the direction in colour space. Currently, there is little behavioural evidence to support these assumptions. This study evaluated the relationship between Δ S and conspicuousness in suprathreshold colours using an Ishihara-style test with a coral reef fish, Rhinecanthus aculeatus . As our measure of conspicuousness, we tested whether fish, when presented with two colourful targets, preferred to peck at the one with a greater Δ S ­ from the average distractor colour. We found the relationship between Δ S and conspicuousness followed­­ a sigmoidal function, with high Δ S colours perceived as equally conspicuous. We found that the relationship between Δ S and conspicuousness varied across colour space (i.e. for different hues). The sigmoidal detectability curve was little affected by colour variation in the background or when colour distance was calculated using a model that does not incorporate receptor noise. These results suggest that the RNL model may provide accurate estimates for perceptual distance for small suprathreshold distance colours, even in complex viewing environments, but must be used with caution with perceptual distances exceeding­ ­10 Δ S .

Automated Digital Image Clustering Algorithm Based on Colour Distance and IDBSCAN

Data clustering is inevitable for crucial data analytic based applications. Though data clustering algorithms are capacious in the literature, there is always a room for efficient data clustering algorithms. This is due to the uncontrollable growth of data and its utilization. The data clustering may consider any of the data formats such as text, images, audio, video and so on. Due to the increasing utilization trend of digital images, this work intends to present a data clustering algorithm for digital images, which is based colour distance and Improvised DBSCAN (IDBSCAN) algorithm. The proposed IDBSCAN completely weeds out the annoying process of setting the initial parameters such as 𝜺 and 𝒎𝒊𝒏𝒑𝒕𝒔 by setting them automatically. The performance of the proposed work is analysed in terms of clustering accuracy, precision, recall, Fmeasure and time consumption rates. The proposed work outperforms the existing approaches with reasonable time consumption.

SoundSight: A Mobile Sensory Substitution Device that Sonifies Colour, Distance, and Temperature

Depth, colour, and thermal images contain practical and actionable information for the visually-impaired. Conveying this information through alternative modalities such as audition creates new interaction possibilities for users as well as opportunities to study neuroplasticity. The ‘SoundSight’ App (www.SoundSight.co.uk) provides a smartphone platform that allows 3D position, colour, and thermal information to directly control thousands of high-quality sounds in real-time to create completely unique and responsive soundscapes for the user. These sounds could be anything - tones, rainfall, speech, instruments, or even full musical tracks. Users have a fine degree of control over how these sounds are presented through controlling the timing and selection of sounds played at a given moment. Through utilising smartphone technology with a novel approach to sonification, the SoundSight App provides a cheap, widely-accessible, scalable, and flexible sensory tool. In this paper we discuss common problems encountered with assistive sensory tools reaching long-term adoption, how our device seeks to address these problems, its theoretical background, its technical implementation, and finally we showcase a range of use case scenarios for scientists, artists, and the blind community.

A generalized equation for the calculation of receptor noise limited colour distances in n -chromatic visual systems

Researchers must assess similarities and differences in colour from an animal's eye view when investigating hypotheses in ecology, evolution and behaviour. Nervous systems generate colour perceptions by comparing the responses of different spectral classes of photoreceptor through colour opponent mechanisms, and the performance of these mechanisms is limited by photoreceptor noise. Accordingly, the receptor noise limited (RNL) colour distance model of Vorobyev and Osorio (Vorobyev & Osorio 1998 Proc. R. Soc. Lond. B 265 , 351–358 ( doi:10.1098/rspb.1998.0302 )) generates predictions about the discriminability of colours that agree with behavioural data, and consequently it has found wide application in studies of animal colour vision. Vorobyev and Osorio (1998) provide equations to calculate RNL colour distances for animals with di-, tri- and tetrachromatic vision, which is adequate for many species. However, researchers may sometimes wish to compute RNL colour distances for potentially more complex colour visual systems. Thus, we derive a simple, single formula for the computation of RNL distance between two measurements of colour, equivalent to the published di-, tri- and tetrachromatic equations of Vorobyev and Osorio (1998), and valid for colour visual systems with any number of types of noisy photoreceptors. This formula will allow the easy application of this important colour visual model across the fields of ecology, evolution and behaviour.