Development Of Double Stage Filter (DSF) On Stereo Matching Algorithm For 3D Computer Vision Applications

In the field of stereo vision, some of existing stereo matching algorithms are designed with less accuracy of algorithm. Thus, a new hybrid algorithm with higher accuracy of computation is developed in this project. This thesis will present the design, development and analysis of performance on a developed Double Stage Filter (DSF) algorithm and other existing stereo matching algorithms. DSF algorithm is a hybrid stereo matching algorithm which divided into two phases. Phase 1 is consists of the part on Sum of Absolute Differences from basic block matching and the part of Scanline Optimization (SO) from dynamic programming approches while phase 2 includes segmentation, merging and basic median filter process. The main feature of DSF algorithm is mainly on the phase 2 or as post-processing in which to remove the unwanted aspects like random noises and horizontal streaks, which is obtained from the raw disparity depth map on the step of optimization. In order to remove the unwanted aspects, two stages filtering process are needed along with the developed approaches in the phase 2 of DSF algorithm. There are two categorized evaluations done on the disparity maps obtained by the algorithms : objective evaluation and subjective evaluation. The objective evaluation includes the evaluation system of Middlebury Stereo Vision website page, computation analysis and traditional methods of Mean Square Errors (MSE), Peak to Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). Besides, for subjective evaluation, the datasets are captured from LNC IP camera and the results obtained by the selected algorithms are evaluated by human's eyes perception. Based on the results of evaluations, the results obtained by DSF is better than the algorithms, basic block matching and dynamic programming

Evaluation of dynamic programming among the existing stereo matching algorithms

There are various types of existing stereo matching algorithms on image processing which applied on stereo vision images to get better results of disparity depth map. One of them is the dynamic programming method. On this research is to perform an evaluation on the performance between the dynamic programming with other existing method as comparison. The algorithm used on the dynamic programming is the global optimization which provides better process on stereo images like its accuracy and its computational efficiency compared to other existing stereo matching algorithms. The dynamic programming algorithm used on this research is the current method as its disparity estimates at a particular pixel and all the other pixels unlike the old methods which with scanline based of dynamic programming. There will be details on every existing methods presented on this paper with the comparison between the dynamic programming and the existing methods. This can propose the dynamic programming method to be used on many applications in image processing

Development of Double Stage Filter (DSF) for Stereo Matching Algorithms and 3D Vision Applications

A part of the stereo matching algorithms development is mainly focused on overcoming unwanted aspects such as noises, unwanted regions and occlusions. In this paper, a new technique which is called Double Stage Filter (DSF) is introduced. This technique is a hybrid algorithm which consists of dynamic programming and block matching. The main feature of DSF is mainly its function at the post-processing stage that is to remove the noises and horizontal stripes, obtained from the raw disparity depth map of dynamic programming. In order to remove the unwanted aspects, a two-stage filtering process is applied. In this DSF algorithm, segmentation process is also required to segment the optimized raw disparity depth map into several parts according to the pixel colours. The first filter block is applied to remove the noises of the segmented parts before merging. Meanwhile, the second filter is used to remove the unwanted region of the outliers on segmented parts after merging processes. The new disparity depth map of DSF is evaluated in Middlebury Stereo Vision page with a few evaluation functions, such as similarity structural (SSIM), peak to signal noise ratio (PSNR) and mean square errors (MSE). At the end of this paper, the performance of DSF is compared with other techniques

Evaluation of stereo matching algorithms and dynamic programming for 3D triangulation

A good result of triangulation or known as Three-Dimensional (3D) is depending on the smoothness of the disparity depth map that obtained from the stereo matching algorithms. The smoother the disparity depth map, the better results of triangulation can be achieved. This paper presents the evaluation of the existing stereo matching algorithms in the aspects of the speed of computational on depth map obtained. The stereo matching algorithms that we applied for experimental purpose are basic block matching, sub-pixel accuracy and dynamic programming. The dataset of stereo images that used for the experimental purpose are obtained from Middlebury Stereo Datasets. This research is to provide an idea on choosing the better stereo matching algorithms to work on the disparity depth map for the purpose of 3D triangulation applications, as the good result of 3D triangulation is depending on how smooth is the disparity depth map can be obtained

Elucidating the genomic architecture of Asian EGFR-mutant lung adenocarcinoma through multi-region exome sequencing

EGFR-mutant lung adenocarcinomas (LUAD) display diverse clinical trajectories and are characterized by rapid but short-lived responses to EGFR tyrosine kinase inhibitors (TKIs). Through sequencing of 79 spatially distinct regions from 16 early stage tumors, we show that despite low mutation burdens, EGFR-mutant Asian LUADs unexpectedly exhibit a complex genomic landscape with frequent and early whole-genome doubling, aneuploidy, and high clonal diversity. Multiple truncal alterations, including TP53 mutations and loss of CDKN2A and RB1, converge on cell cycle dysregulation, with late sector-specific high-amplitude amplifications and deletions that potentially beget drug resistant clones. We highlight the association between genomic architecture and clinical phenotypes, such as co-occurring truncal drivers and primary TKI resistance. Through comparative analysis with published smoking-related LUAD, we postulate that the high intra-tumor heterogeneity observed in Asian EGFR-mutant LUAD may be contributed by an early dominant driver, genomic instability, and low background mutation rates

Integrative Profiling of T790M-Negative EGFR-Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities

Purpose: Despite the established role of EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutated NSCLC, drug resistance inevitably ensues, with a paucity of treatment options especially in EGFRT790M-negative resistance. Experimental Design: We performed whole-exome and transcriptome analysis of 59 patients with first- and second-generation EGFR TKI-resistant metastatic EGFR-mutated NSCLC to characterize and compare molecular alterations mediating resistance in T790M-positive (T790M(+)) and -negative (T790M(-)) disease. Results: Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M(-) tumors, orthogonally validated using multiplex IHC. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole-genome doubling and nonaging mutational signatures in T790M(-) tumors. Almost half of resistant tumors were further classified as immune(hot), with clinical outcomes conditional on immune cell-infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M(-) and T790M(+) disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naive patients. Conclusions: Our results illustrate the interplay between genetic alterations, cell lineage plasticity, and immune microenvironment in shaping divergent TKI resistance and outcome trajectories in EGFR-mutated NSCLC. Genomic and transcriptomic profiling may facilitate the design of bespoke therapeutic approaches tailored to a tumor's adaptive potential

Genomic landscape of lung adenocarcinoma in East Asians

Lung cancer is the world's leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts. Genomic and transcriptomic analysis of lung adenocarcinoma (LUAD) in Asia indicates that Asian LUADs have fewer mutations, lower driver prevalence and fewer copy number alterations than European LUADs