CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the "early vision" stage f...

This paper presents the possibility of using image completion combined with a large image database to create an infinite panorama. The algorithm performs scene matching using a portion of the original input image to find the best matching neighboring scenes and then composites these images in a seamless way. Even though the automatically generated panoramas may not be convincingly realistic, th...

Recovering a dense depth image from sparse LiDAR scans is challenging task. Despite the popularity of color-guided methods for sparse-to-dense completion, they treated pixels equally during optimization, ignoring uneven distribution characteristics in map and accumulated outliers synthesized ground truth. In this work, we introduce uncertainty-driven loss functions to improve robustness complet...

Image Inpainting is the process of filling in missing regions in an image. The objective of inpainting is to reconstruct the missing regions in a visually plausible way. Several algorithms are available in the literature for the same. Many researchers have proposed a large variety of exemplar based image inpainting algorithms to restore the structure and texture of damaged images. In this paper...

Image Inpainting is the process of filling in missing regions in an image. The objective of inpainting is to reconstruct the missing regions in a visually plausible way. Several algorithms

We reanalyzed data reported in previous mental imagery experiments (D’Angiulli, 2001; D’Angiulli & Reeves, 2002). The data consisted of self-reported image vividness ratings (1 = no image; 7 = perfectly vivid) and image latencies (image completion RTs) for “small” (i.e., subtending < 16) and “large” (i.e., > 16) images, in two image generation conditions: trial-unique and repeated. In the origi...

Most natural images can be approximated using their low-rank components. This fact has'been successfully exploited in recent advancements of matrix completion algorithms for image recovery. However, a major limitation of low-rank matrix completion algorithms is that they cannot recover the case where a whole row or column is missing. The missing row or column will be simply filled as an arbitra...

Object completion is an inherent property of visual recognition in which objects can be accurately perceived in the presence of substantial obstructions. We have previously shown [Cereb. Cortex 12 (2002) 163] that high-order human object areas are driven partially by local object fragments and partially by global completion effects. Here we explored, through a backward masking paradigm, whether...

The senior research project that was completed was a study in the field of remote sensing and research area of image fusion technique development. Image fusion is sometimes referenced as image merging in the literature on the subject. The image fusion techniques that were developed for the project were implemented using digital image processing methods. An image fusion algorithm with two main p...

From an image we construct an invertible orientation score, which provides an overview of local orientations in an image. This orientation score is a function on the group SE(2) of both positions and orientations. It allows us to diffuse along multiple local line segments in an image. The transformation from image to orientation score amounts to convolutions with an oriented kernel rotated at m...