In depth discontinuous and untextured regions, depth maps created by multiple view stereopsis are with heavy noises, but existing depth map fusion methods cannot handle it explicitly. To tackle the problem, two novel strategies are proposed: 1) a more discriminative fusion method, which is based on geometry consistency, measuring the consistency, and stability of surface geometry computed on both partial and global surfaces, different from traditional methods only using visibility consistency; 2) a graph optimization method which fuses pyramids of depth maps as mutual complementary information is available in different scales, and differs from existing multi-scale fusion methods. The method considers both sampling scale of a point and relations among points, and is proven to be solvable by graph cuts. Experimental results verify the superior performance of the proposed method to the traditional visibility consistency-based methods, and the proposed method is also compared favorably with a number of state-of-the-art methods. Moreover, the proposed method achieves the highest completeness among all the methods compared.

The seed-and-expand scheme is appropriate for multiple view stereo, since it can build dense point clouds adaptively by avoiding unnecessary computation. However, due to the irregularity of the algorithm, it is not suitable for parallel computing on general public utilities (GPU). This paper is the first attempt to implement the irregular seed-and-expand method on GPU for multiple view stereo problems. Meanwhile, a hierarchical parallel computing architecture is also proposed to maximize the usage of both CPU and GPU. The adaptivity of the seed-and-expand scheme is pushed further by processing a pixel several rounds while, in order to maintain regularity for GPU implementation, every seed has exactly the same behavior in a single round of optimization. The high adaptivity also improves the robustness of the proposed method, thus aggressive matching score and a view selection method can be used to improve the reconstruction completeness extensively, without smearing out local details and lowering the accuracy. Compared with the state of the art, the proposed method achieves higher accuracy and completeness on standard datasets. The proposed method is also very fast. It is maximally five times faster than other methods running on a CPU and is on par with the regular depth map-based methods on GPU, which are naturally suitable for GPU acceleration.

When using fixed-window phase correlation (PC) to estimate the disparity of stereo images, the precision is usually rather poor due to large depth differences of scenes and noise, and this problem is specially severe when using unmanned aerial vehicle (UAV) image pairs to extract the digital elevation model of mountain land. To tackle this problem, this paper proposes a hierarchical and adaptive PC, which includes three steps: First, PC with the initialized window is performed to coarsely estimate a disparity value, along with the peak of the Dirichlet function for each pixel; then, an additional round of PC is performed for each pixel using the window of smaller size and with being guided by the coarsely estimated disparity; finally, the previous two steps are iteratively performed until convergence. In particular, using the peak of the Dirichlet function of each pixel in step two, we can drop out the influence of dramatically changing areas such as river; moreover, the scheme can minimize the influence of boundary overreach. The novel scheme has been tested on a large number of UAV images captured at mountainous regions in southwest China, showing that the proposed method is superior to the state-of-the-art methods, especially in handling UAV images of the high mountains and rivers.

The L1-norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function to the original one can be obtained by tuning the parameters contained in the mollifier. Next, a recurrent neural network is proposed to optimize the mollified function, which will converge to a local minimum. In addition, to boost the speed of the system, the mollifying process is implemented by a filtering procedure. The influence of two mollifier parameters is theoretically analyzed and experimentally confirmed, showing that one of the parameters is critical to computational efficiency and accuracy, while the other not. A large number of experiments on synthetic data show that the proposed method is competitive to the state-of-the-art methods. In particular, the experiments on large matrices and a real application in the structure from motion indicate that the memory requirement of the proposed algorithm is mild, making it suitable for real applications that often involve large-scale matrix decomposition.

Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule. To address the problem of feature changes due to different speckle sizes, TRUS images are split into several arc-like strips. In each strip, every individual feature vector is sparsely represented, and representation residuals are obtained. The residuals, along with the spatial coherence inherited from biological tissues, are combined to segment the prostate preliminarily via graph cuts. After that, the segmentation is fine-tuned by a novel level sets model, which integrates (1) the prostate shape prior, (2) dark-to-light intensity transition near the prostate boundary, and (3) the texture feature just obtained. The proposed method is validated on two 2-D image datasets obtained from two different sonographic imaging systems, with the mean absolute distance on the mid gland images only 1.06±0.53 mm and 1.25±0.77 mm, respectively. The method is also extended to segment apex and base images, producing competitive results over the state of the art.