Shape Recovery by Focus Based Methods Using Low Pass Filter

Shape from focus (SFF) is a technique used to estimate the depth of a scene from a sequence of multifocus images. Existing SFF methods can be categorized into two groups: traditional methods and deep learning-based methods. Traditional methods typically employ a focus measure (FM) operator to assess the sharpness of individual pixels in a single-frame image, often overlooking the associations within the image sequence. Deep learning methods generally rely on labeled datasets, which are often challenging to obtain in real-world scenarios. Based on these observations, we propose a novel sequence association-based (SAS) framework aimed at enhancing the generalizability of SFF methods. In the SAS framework, an image sequence is treated as complete three-dimensional (3D) data throughout the processes of multiview decomposition, selective fusion and multiscale feature aggregation. Furthermore, the framework includes a tighter multiview learning generalization error bound to guide the development of the selective fusion method. This method leverages isomorphisms among multiple views to effectively mitigate the adverse effects of outlier noise on the reconstruction of various scenes. Comprehensive experiments on seven synthetic datasets and two real scenes with unknown labels demonstrate the effectiveness and generalizability of the SAS framework compared to state-of-the-art SFF methods.

In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.

Shape from Focus (SFF) is one of passive optical methods for estimating 3D shape of an object. In SFF, a large number of 2D images with different focus levels are required. The number of images may affect the complexity and the accuracy of the results. In this manuscript, a Gaussian process regression (GPR) method is proposed to get 3D shape from the minimum number of 2D images. The proposed method (SFF.GPR) is applied to fit focus curves, which are obtained by applying one of focus measure operators. Experimental results demonstrate the effectiveness of the proposed method.

Reliability measure for shape-from-focus

Application of optical 3D measurement on thin film buckling to estimate interfacial toughness

The use of intelligent and sophisticated approaches in the domain of computer vision and pattern recognition is consistently increasing. This paper introduces a novel machine learning based approach for Shape From Focus (SFF), where the in-focus pixels are selected from a sequence of images. In contrast to computing focus values directly in spatial or transform domain, the proposed method first nonlinearly transforms the input space into feature space and then computes the focus value by transforming the data into eigenspace. First, the nonlinear transformation is performed by using kernel function and then Principal Component Analysis (PCA) is applied. This idea is also supported by the fact that out-of-focus is analogous to blurring and is a nonlinear phenomenon. An initial depth map is computed by maximizing the focus measure. To further refine the 3D shape, bilateral filter is applied. The proposed method is experimented using synthetic and real image sequences. The results demonstrate the effectiveness and the robustness of the new method.

The most popular shape from focus (SFF) methods in the literature are based on the concept of focused image surface (FIS)-the surface formed by the best focus points. According to paraxial-geometric optics, there is one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of three-dimensional (3-D) shape recovery from image focus can be described as the problem of determining the shape of the FIS. The conventional SFF method is inaccurate because of piecewise constant approximation of the FIS. The SFF method based on the FIS has shown better results by exhaustive search of the FIS shape using planar surface approximation at the cost of considerably higher computations. In this paper, search of the FIS shape is presented as an optimization problem, i.e., maximization of the focus measure in the 3-D image volume. The proposed method searches the optimal focus measure in the whole image volume, instead of the small volume as adopted in previous methods. The dynamic programming, instead of the approximation techniques, is used to search the optimal FIS shape. A direct application of dynamic programming on a 3-D data is impractical, because of higher computational complexity. Therefore a fast heuristic model based on dynamic programming is proposed for the search of FIS shape. The shape recovery results of the new method are better than previous methods. The proposed algorithm is significantly faster than the FIS algorithm, but a little slower than the conventional algorithm.

Shape reconstruction from focus and image fusion

Shape-from-focus reconstruction using nonlocal matting Laplacian prior followed by MRF-based refinement

In shape-from-focus (SFF) methods, a single focus measure is used to compute the focus volume. However, it seems that a single focus measure operator is not capable of computing accurate focus values for the images of diverse types of object shapes. Furthermore, most of the SFF methods try to improve the depth map without considering any additional structural or prior information. Consequently, the extracted shape of the object might lack important details. In this work, we address these problems and suggest a method in which depth hypotheses are combined for a more accurate 3D shape through 3D weighted least squares. First, depth hypotheses are obtained by applying a number of focus operators. Then, structural prior or guidance volume is extracted from the focus measure volumes. Finally, a 3D weighted least squares optimization technique is applied to the depth hypothesis volume, where weights are computed from the guidance volume. Thus, by inducing structural prior, an improved resultant depth map is obtained. The proposed method was tested using various image sequences of synthetic and microscopic real objects. Experimental results and comparative analysis demonstrated the effectiveness of the proposed method.

In the areas of computer vision and computer graphics, analyzing information about three-dimensional objects from two-dimensional images is essential. Shape from shading is a very fundamental problem in computer vision, which uses features and patterns in images to infer the shape of the object in our viewing direction. The influence of solving this problem is significant. For example, a typical application of shape from shading is in astronomy, in which we can obtain landscape features of a planet by analyzing the information from the image we get.

Background:Polyp shapes play an important role in colorectal diagnosis. However, endoscopy images are usually composed of nonrigid objects such as a polyp. Hence, it is challenging for polyp shape recovery. It is demanded to establish a support system of the colorectal diagnosis system based on polyp shape.Introduction:Shape from Shading (SFS) is one valuable approach based on photoclinometry for polyp shape recovery. SFS and endoscope image are compatible on the first sight, but there are constraints for applying SFS to endoscope image. Those approaches need some parameters like a depth from the endoscope lens to the surface, and surface reflectance factor . Furthermore, those approaches assume the whole surface which has the same value of for the Lambertian surface.Methods:This paper contributes to mitigating constraint for applying SFS to the endoscope image based on a cue from the medical structure. An extracted medical suture is used to estimate parameters, and a method of polyp shape recovery method is proposed using both geometric and photometric constraint equations. Notably, the proposed method realizes polyp shape recovery from a single endoscope image.Results:From experiments it was confirmed that the approximate polyp model shape was recovered and the proposed method recovered absolute size and shape of polyp using medical suture information and obtained parameters from a single endoscope image.Conclusion:This paper proposed a polyp shape recovery method which mitigated the constraint for applying SFS to the endoscope image using the medical suture. Notably, the proposed method realized polyp shape recovery from a single endoscope image without generating uniform Lambertian reflectance.

IntroductionThe American Society of Gastroenterology Endoscopy led Preservation and Incorporation of Valuable Endoscopic Innovations initiative has identified real time polyp diagnosis as one of the next major technology-driven changes in...

We introduce photometric stereo reconstruction for surface analysis of mucosal tissue. Photometric stereo captures images of a surface from a single camera, but illuminated from different light sources. From the set of images, a detailed surface reconstruction is estimated. We apply photometric stereo principles to mucosal tissue, which raises technical challenges due to its specular appearance. Following reconstruction of the surface, we apply surface analysis techniques based on differential geometry to detect regions that are locally spherical, suggestive of polyps. The research described in this paper is a first step towards a real-time clinical support system based on 3D reconstruction of human tissue for gastereoentrologic applications such as colonoscopy.

Unmanned Surface Vehicles (USV) involves vehicle structure, computer science, robotic vision, automatic control and other areas, so the research on USV is significant in both theoretical and practical aspects. Vessel detection and recognition and tracking is mainly discussed in this paper by fusing data coming from a variety of sensors: a single camera, a radar, an inertial sensor. The vision is used to find the true targets detected by the radar and to discard those who might be false positives by the method of combining offline SVM classifier, saliency detection and online compressive tracking. Additionally, heading reference system (HRS) is used for compensating the angle deviation caused by the shaking of USV due to the fluctuation of waves. The true targets will be considered by the comprehensive decision of SVM classifier and radar cross-validation results and saliency detection validation radar results. Experiments confirm the effectiveness of the proposed architecture.