Multicoloured Jacquard artworks reproduction with C, M, Y, and K channels modification to improve weave colour accuracy

Multicoloured Jacquard artwork reproduction has been restricted by the modern setting of weaving machinery. To resolve the current limitations, innovative weaving applications have been introduced. The subtractive CMYK system used for colour printing has been employed for multi weave colour reproduction as a wide scope of a weave colour creation is possible by utilizing a small number of weft yarn colours. In use of cyan [C], magenta [M] and yellow [Y] coloured yarns, a range of CMYK secondary colours (red [R], green [G] and blue [B]) production is feasible by juxtaposing a pair of the three yarn colours. In addition, controlling chroma levels of the primary colours is viable by mixing with a black yarn. However, there are variations between CMYK colour mixing and optical yarn colour mixing due to the material differences. Therefore, modifications of the [C], [M], and [Y] colour channels are required to reproduce tertiary colours such as a black colour. This is because opaque and non-blendable yarns are used to create weave colours and therefore, exhibited yarn colours are all perceived together. In use of image processing tools offered by Adobe Photoshop, a pair of the [C], [M], [Y], and [K] colour channels are merged to individually generate the primary ([C], [M], [Y]) and secondary ([R], [G] and [B]) colour channels. In the process, a pair of C, M, Y and K channels is combined based on mathematical functions. As a result, new six colour channels ([C], [M], [Y], [R], [G], and [B]) are created to improve weave colour reproduction accuracy. This study introduces details of the colours segmentation processes and weaving experiment results that examines the significance of the newly developed the colour channels for multicoloured artwork reproduction.</p

Compelling Interlaced Colours

The digital technology adopted in weaving industry has greatly enhanced production efficiency. However, we have compromised many artistic and colour values that traditional weaving has been offered to us. We have been exploring the physiological optical illusion of juxtaposed primary-coloured yarns to overcome the current limitations in woven textile coloration. The existing Jacquard machinery is restricted to supply the number of yarn colours due to its performance characteristics that limits multi-colour reproduction. Over the last 10 years extensive weaving experiments have been conducted to expand a feasible weave colour scope by using only a small number of primary-coloured yarns. Various colour theories, digital images, and traditional weave structures have been tested to prove. Through this exhibition, we introduce the first kind woven Jacquard textiles that encompass colour, material, and texture initiation to make a valuable contribution to current woven textile coloration and design methods.Exhibited at The Fashion Gallery, Jockey Club Innovation Tower, PolyU, Hong Kong, 3 - 20 March 2023.</p

A top-down manner-based DCNN architecture for semantic image segmentation

<div><p>Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.</p></div

<i>In situ</i> hybridization for Bad mRNA in the RA, LMAN, HVC and Area X of the Bengalese finch.

<p>A1–D2: Labeled cells in the RA at post-hatching day (P) 45 (A1, A2), the LMAN at P35 (B1, B2), the HVC at P45 (C1, C2), and in Area X at P35 (D1, D2). E-H: Comparisons of the densities of Bad mRNA-positive cells in the RA (E), LMAN (F), HVC (G) and Area X (H) between males and females. Borders of the song nuclei (dashed lines) were determined with the help of another set of Nissl-stained sections. The Nissl-defined border of the female Area X was difficult to clearly identify, and the dashed lines in D2 indicate the approximate region corresponding to the male Area X. Dorsal is up and caudal is right. Scale bar = 200 μm in A1–C2 and 300 μm in D1–D2. The data are expressed as the mean ± SEM.</p