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- Robust Depth Estimation From Multi-Focus Plenoptic ImagesPublication . Cunha, Francisco; Thomaz, Lucas; Tavora, Luis M. N.; Assunção, Pedro A. A.; Fonseca-Pinto, Rui; Faria, Sérgio M. M.This paper describes a robust depth estimation algorithm for multi-focus plenoptic images. The main feature of the proposed method consists of a hybrid template matching scheme built-upon intensity and local phase information, which adapts to the blurriness of neighbouring lenslet microimages. By reducing the impact of defocusblur on the template matching accuracy, the proposed method efficiently handles the varying triangulation baseline over the depth-of-field, thus discarding the need for scene-related information such as the expected range of disparities. Experimental results demonstrate the robustness of the proposed method over the most used commercially available depth estimation algorithm, achieving a reduction of 73% on the depth estimation error.
- 4D Light Field Disparity Map estimation using Krawtchouk PolynomialsPublication . Lourenco, Rui; Rivero-Castillo, Daniel; Thomaz, Lucas A.; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Faria, Sergio M. M. deThis work presents an improved method to estimate disparity maps obtained from light field cameras using a novel edge detection algorithm based on Krawtchouk polynomials. The proposed method takes advantage of these polynomials to determine gradient information and find the edges based on automatically estimated weak and strong thresholds. The calculated edges in the gray scale epipolar plane image representation of a light field are then used to improve the accuracy of object boundaries in the the disparity map. The proposed method achieves better results when compared to other edge detection algorithms, both in terms of objective and subjective quality, specifically by reducing the mean squared error and the artifacts in the object boundaries. Furthermore, on average, the proposed method outperforms the state-of-the-art depth estimation algorithms, in terms of the objective quality of the final disparity map, namely for the commonly used HCI dataset.
- Disparity compensation of light fields for improved efficiency in 4D transform-based encodersPublication . Santos, Joao M.; Thomaz, Lucas A.; Assuncao, Pedro A. A.; Cruz, Luis A. da Silva; Tavora, Luis M. N.; Faria, Sergio M. M. deEfficient light field en coders take advantage of the inherent 4D data structures to achieve high compression performance. This is accomplished by exploiting the redundancy of co-located pixels in different sub-aperture images (SAIs) through prediction and/or transform schemes to find a m ore compact representation of the signal. However, in image regions with higher disparity between SAIs, such scheme's performance tends to decrease, thus reducing the compression efficiency. This paper introduces a reversible pre-processing algorithm for disparity compensation that operates on the SAI domain of light field data. The proposed method contributes to improve the transform efficiency of the encoder, since the disparity-compensated data presents higher correlation between co-located image blocks. The experimental results show significant improvements in the compression performance of 4D light fields, achieving Bjontegaard delta rate gains of about 44% on average for MuLE codec using the 4D discrete cosine transform, when encoding High Density Camera Arrays (HDCA) light field images.
- Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative studyPublication . Pereira, Pedro M. M.; Fonseca-Pinto, Rui; Paiva, Rui Pedro; Assuncao, Pedro A. A.; Tavora, Luis M. N.; Thomaz, Lucas A.; Faria, Sergio M. M.Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which exists since dermatologist do not delineate lesions for segmentation but for extraction) and also significant inter- and intra-individual variability, such segmentation is not sufficiently accurate for segmentation studies. This work addresses these limitations to enable detailed analysis of lesions’ geometry along with extraction of non-linear characteristics of region-of-interest border lines. A comprehensive review of 39 segmentation methods is carried out and a contribution to improve dermoscopic image segmentation is presented to determine the regions-of-interest of skin lesions, through accurate border lines with fine geometric details. This approach resorts to Local Binary Patterns and k-means clustering for precise identification of lesions boundaries, particularly the melanocytic. A comparative evaluation study is carried out using three different datasets and reviewed algorithms are grouped according to their approach. Results show that algorithms from the same group tend to perform similarly. Nevertheless, their performance does not depend uniquely on the algorithm itself but also on the underlying dataset characteristics. Throughout several evaluations, the proposed Local Binary Patterns method presents, consistently, better average performance than the current state-of-the-art techniques across the three different datasets without the need of training or supervised learning steps. Overall, apart from presenting a new segmentation method capable of outperforming the current state-of-the-art, this paper provides insightful information about the behaviour and performance of different image segmentation algorithms.