Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark.

In this article, we provide a comprehensive study of a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8528 high-quality and elaborately selected images with object mask annotations, covering five superclasses and 70 subclasses. The dataset spans a wide range of natural and artificial camouflage scenes with diverse object appearances and backgrounds, making it a very challenging dataset for CoCOD. Besides, we propose the first baseline model for CoCOD, named bilateral-branch network (BBNet), which explores and aggregates co-camouflaged cues within a single image and between images within a group, respectively, for accurate camouflaged object detection (COD) in given images. This is implemented by an interimage collaborative feature exploration (CFE) module, an intraimage object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art (SOTA) models, including 12 COD algorithms and six CoSOD algorithms, on the proposed CoCOD8K dataset under five widely used evaluation metrics. Extensive experiments demonstrate the effectiveness of the proposed method and the significantly superior performance compared to other competitors. We hope that our proposed dataset and model will boost growth in the COD community. The dataset, model, and results will be available at: https://github.com/zc199823/BBNet-CoCOD.

Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range Query.

Range query is the hot topic of the privacy-preserving data publishing. To preserve privacy, the large range query means more accumulate noise will be injected into the input data. This study presents a research on differential privacy for range query via Haar wavelet transform and Gaussian mechanism. First, the noise injected into the input data via Laplace mechanism is analyzed, and we conclude that it is difficult to judge the level of privacy protection based on the Haar wavelet transform and Laplace mechanism for range query because the sum of independent random Laplace variables is not a variable of a Laplace distribution. Second, the method of injecting noise into Haar wavelet coefficients via Gaussian mechanism is proposed in this study. Finally, the maximum variance for any range query under the framework of Haar wavelet transform and Gaussian mechanism is given. The analysis shows that using Haar wavelet transform and Gaussian mechanism, we can preserve the differential privacy for each input data and any range query, and the variance of noise is far less than that just using the Gaussian mechanism. In an experimental study on the dataset age extracted from IPUM's census data of the United States, we confirm that the proposed mechanism has much smaller maximum variance of noises than the Gaussian mechanism for range-count queries.

Comment on "Fringe projection profilometry with nonparallel illumination: a least-squares approach".

We comment on the recent Letter by Chen and Quan [Opt. Lett.30, 2101 (2005)] in which a least-squares approach was proposed to cope with the nonparallel illumination in fringe projection profilometry. It is noted that the previous mathematical derivations of the fringe pitch and carrier phase functions on the reference plane were incorrect. In addition, we suggest that the variation of carrier phase along the vertical direction should be considered.

Out-of-plane shape determination in generalized fringe projection profilometry.

The out-of-plane shape determination in a generalized fringe projection profilometry is presented. The proposed technique corrects the problems in existing approaches, and it can cope well with the divergent illumination encountered in the generalized profilometry. In addition, the technique can automatically detect the geometric parameters of the experimental setup, which makes the generalized fringe projection profilometry simple and practical. The concept was verified by both computer simulations and actual experiments. The technique can be easily employed for out-of-plane shape measurements with high accuracies.

Class of 4 + 1-phase algorithms with error compensation.

It is well known that phase-shifting interferometry suffers from inaccuracy in the presence of phase-shifting errors. We have proved the limitation of using a 4-phase algorithm to reduce the phase-measurement error in the presence of the phase-shifting error. A class of 4 + 1-phase error compensating algorithms is formulated. It is shown that the proposed algorithms can effectively minimize the effects of the constant phase-shifting error and possess a superior performance than existing error-compensating algorithms. The effectiveness of the proposed algorithm is demonstrated by computer simulations and experiments.