Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the process of straightening, resulting in feature changes, loss of details, and poor generalization. To solve these problems, this paper proposes a novel straightening method based on disentanglement representation learning. Our method consists of two main components: the Disentanglement Representation Encoder (DRE) and the Straightening Generator (SG), where DRE discovers and disentangles the bent representation and the content representation in the latent space, while SG is used to generate the straightened chromosome images based on the disentangled representations. Leveraging the bent representation and the content representation disentangled by DRE, our method produces the straightened representation by reducing the bent representation while keeping the content representation unchanged, making straightening chromosome without changing its banding patterns possible. Evaluation results on both the Frechet Initiation Distance (FID) and the Downstream Classification Accuracy (DCA) metrics show that our method achieves good performance.

Chromosome instance segmentation plays a crucial role in chromosomal karyotype analysis. However, the overlapping of chromosome instances and their individual morphological differences make accurate chromosome instance segmentation a challenging task. Especially in handling overlapping chromosome instances, traditional segmentation methods tend to confuse instances with one another. To solve these problems, this paper proposes an innovative method named Adaptive2Former. It builds upon our novel devised Adaptive Query Decoder (AQD) module to enhance segmentation precision. The AQD effectively utilizes the [cls] token from the backbone network to dynamically generate adaptive query vectors instead of using fixed queries. This new design leverages the semantic information inside the input images, producing representations more conducive to subsequent segmentation module, thereby improving the model’s segmentation performance. Experiments conducted on our dataset demonstrate that the proposed Adaptive2Former significantly enhances the performance of chromosome instance segmentation compared to Mask2Former and other existing models, achieving results of 97.65% mAP75 and 0.21 Dice Loss.

Human chromosomes carry genetic information about our life. Chromosome classification is crucial for karyotype analysis. Existing chromosome classification methods do not take into account reasoning, such as: analyzing the relationship between variables, modeling uncertainty, and performing causal reasoning. In this paper, we introduce a knowledge engine for reasoning-based human chromosome classification that stores knowledge of chromosomes via a novel representation structure, the Chromosome Part Description (CPD), and reasons over CPDs by utilizing the probability tree model (PTM) for classification. Each CPD keeps information on a particular feature of chromosomes, while the PTM provides causal reasoning capability taking CPDs as nodes and dependencies between CPDs and types as edges. Experimental results show that the proposed knowledge engine’s performance increases when providing more CPDs and achieves 100% classification accuracy with more than three CPDs.

Abstract Face image super-resolution imaging is an important technology which can be utilized in crime scene investigations and public security. Modern CNN-based super-resolution produces excellent results in terms of peak signal-to-noise ratio and the structural similarity index (SSIM). However, perceptual quality is generally poor, and the details of the facial features are lost. To overcome this problem, we propose a novel deep neural network to predict the super-resolution wavelet coefficients in order to obtain clearer facial images. Firstly, this paper uses prior knowledge of face images to manually emphases relevant facial features with more attention. Then, a linear low-rank convolution in the network is used. Finally, image edge features from canny detector are applied to enhance super-resolution images during training. The experimental results show that the proposed method can achieve competitive PSNR and SSIM and produces images with much higher perceptual quality.

In this paper, we propose a fast single image dehazing method based on positive correlation. Firstly, a linear model is built to describe the positive correlation between the minimum channel of the hazy image and its corresponding depth map. Then, the transmission map and the atmospheric light are separately obtained using the created linear model. Finally, based on the traditional atmospheric scattering model, the haze-free image can be recovered with the transmission map and the atmospheric light. Experimental results on numerous hazy images demonstrate that proposed method has better performance and lower time complexity than the state-of-the-art methods.

Pervasive computing environments (PvCE) are embedded with interconnected smart devices which provide users with services desired. To meet requirements of users, smart devices with different kinds of functions may need to be associated together to provide the service described in the user requirement, which is called service composition. As the service composition environment may be dynamic and large scale, centralized service composition algorithm is usually inefficient due to message cost. On the other hand, a decentralized approach, which employs pre-determined coordinators to search and compose service, may have high cost as well. In this paper, we discuss a localized approach for service composition based on the Ubiquitous Interacting Object (UIO) model we have proposed earlier. UIO is an abstraction of physical devices in PvCE with ability to find and collaborate with other devices through exposing their capabilities as services. In our localized service composition algorithm (LASEC), UIOs collaborate with each other in a bottom-up, localized manner to compose required service without requiring global knowledge. To solve the problem of blind compositions in LASEC, we propose a novel mechanism called Alien-information-based Acknowledging (A-Ack), in which a UIO decides on collaborating with another UIO only after obtaining some additional information from the collaboration candidate. Specifically, this information refers to ability of a given UIO to compose another part of the service. Proposed LASEC is message-efficient and quality-guaranteed. Extensive simulations of LASEC as well as existing decentralized and pull-based centralized algorithms have been conducted. The results show the relatively low communication cost and composition time of LASEC. Moreover, we demonstrate feasibility of our approach with a prototype implementation.

An improved view interpolation scheme with depth reliability maps is presented to enhance the quality of synthesized images in this paper. First of all, the left depth map and the right one can be estimated by a graph cut approach from the input reference images. Then, the predict depth maps are derived by forward mapping, and then a median filter is applied to eliminate small blank points caused by forward mapping. Next, the left predict image and the right one are synthesized by reverse mapping using the reference image with the associated predict depth map. After that, in order to obtain better visual quality, the post-processing operations are developed. On the one hand, the segmentation mask can be derived from the depth reliability maps, which are produced from the depth maps. On the other hand, based on the segmentation mask, a virtual view image is generated by a weighted interpolation scheme from the left and right predict images.

In this paper, a novel disocclusion scheme is proposed based on the depth reliability maps for virtual view synthesis. One important aspect of the developed method is that the occlusion map is estimated with the depth reliability maps. Therefore, the holes in a virtual view image can be filled in by blending the two predict images based on the occlusion map. The experimental results demonstrate that the addressed method has better performance for view synthesis than the other three conventional methods through the assessment of image quality and algorithm cost.

We aim to bridge the gap between requirement&design (R&D) documents and source code to support fast code changes. Recognizing that developers are used to working on source code, we model R&D documents as semi-structured LATEX documents and distribute them closer to related source code. By doing this, working with all kinds of artifacts is unified. Developers can edit, search and compare all the artifacts in environments that they are familiar with. This solution also supports document generation, code generation, and other extensions.