News

Camouflage detection is a pivotal area that relies on sophisticated algorithms and advanced models capable of discerning objects or individuals concealed within their environment by employing various camouflage techniques like pattern matching. These models play a crucial role in diverse domains such as security, wildlife monitoring, and even military applications, where their ability to distinguish foreground hidden objects from the background image pixels is paramount for effective detection and identification.

In our pursuit of enhancing camouflage detection capabilities, we have trained our model primarily on hidden animals. We have chosen to use the COD10K dataset [1]. This consists of 10,000 images, of which 5,066 are camouflaged, 3000 are background, and 1934 are non-camouflaged, which makes it a robust dataset very suitable for camouflage detection.

Currently, we have a GreenCOD[2] model designed with EfficientNet feature extraction and an upsampling structure of XGBoost to gradually predict the detection results. Our goal is to further reduce the model size and runtime, while potentially increasing the performance. In the future, we plan to use this improved model to analyze videos with hidden objects, like the MoCA-Mask dataset. This will help the model work well in real-time situations, like surveillance and protecting wildlife. Ultimately, we aim to enhance camouflage detection systems used in different fields.

[1] D. Fan, G. Ji, G. Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao. “Camouflaged Object Detection”. CVPR, 2020.

[2] H. Chen, Y. Zhu, S. You, C.-C. J. Kuo, “GreenCOD: Green Camouflaged Object Detection”. APSIPA Transactions on Signal and Information Processing 2023. Available: https://hongshuochen.com/GreenCOD/

Image credits:

Image showing image/mask pair of animal image is from [1].

Image showing the architecture of GreenCOD is from [2].

For more than two decades, the Thanksgiving Luncheon has been an unmissable tradition at MCL. A cherished gathering that brings the entire group together for a joyous meal. This year was no exception, as on November 23, 2023, the MCL family gathered at Shiki Seafood Buffet to celebrate this annual event.
The Thanksgiving Luncheon holds a special place in the hearts of all MCL members. It’s not just a meal; it’s a chance for the community to bond, laugh, and share in the spirit of gratitude. The food was not the only highlight of the day; it was the connections and conversations that truly enriched the gathering. As colleagues and friends sat together, conversations flowed effortlessly, fostering deeper connections and allowing everyone to unwind after a hectic semester.
The success of this cherished event wouldn’t have been possible without the dedicated efforts of Professor Kuo, whose vision and commitment brought everyone together, and the students, whose meticulous planning ensured a seamless and enjoyable experience for all.
Here’s to many more years of laughter, camaraderie, and cherished memories. Happy Thanksgiving!

Congratulations to Max Chen for passing his defense today. Max’ thesis is titled “A Green Learning Approach to Deepfake Detection and Camouflage and Splicing Object Localization.” His Dissertation Committee includes Jay Kuo (Chair), Shrikanth Narayanan, and Aiichiro Nakano (Outside Member). The Committee members highly praised the quality of his work. MCL News team invited Max for a short talk on her thesis and PhD experience, and here is the summary. We thank Max for his kind sharing, and wish him all the best in the next journey.

“In the current technological era, the advancement of AI models has not only driven innovation but also heightened concerns over environmental sustainability due to increased energy and water usage. For context, the water consumption equivalent to a 500ml bottle is tied to 10 to 50 responses from a model like GPT-3, and projections suggest that by 2027, AI could be using an estimated 85 to 134 TWh per year, potentially surpassing the water withdrawal of half of the United Kingdom. In light of these challenges, there is an urgent call for AI solutions that are environmentally friendly, characterized by lower energy consumption through fewer floating-point operations (FLOPs), more compact designs, and the ability to run independently on mobile devices without depending on server-based infrastructures.

This thesis introduces a novel approach for Camouflaged Object Detection, termed “GreenCOD.” GreenCOD combines the power of Extreme Gradient Boosting (XGBoost) with deep features. Contemporary research often focuses on devising intricate DNN architectures to enhance the performance of Camouflaged Object Detection. However, these methods are typically computationally intensive and show marginal differences between models. Our GreenCOD model stands out by employing gradient boosting for detection tasks. With its efficient design, it requires fewer parameters and FLOPs [...]

Demosaicing is a crucial step in the process of converting raw image data captured by sensors with Bayer color filter arrays into a full-color image that humans can perceive. Bayer arrays are a type of color filter array commonly used in digital image sensors, named after Bryce Bayer, who patented the design. These arrays consist of a grid of photosites, each covered by a color filter—typically red, green, or blue. Please see Figure 1.

Demosaicing in real-world scenarios poses a significant challenge due to the emergence of artifacts caused by factors such as sensor noise, motion blur, and edge artifacts. Sensor noise, introduced during image acquisition, can manifest as color noise, luminance noise, and texture loss in demosaiced images.

Another hurdle in demosaicing involves the necessity for ground truth data. Obtaining precise RGB images for training machine learning-based demosaicing methods is time-consuming, requiring meticulous alignment and synchronization across multiple color channels. To overcome this challenge, our strategy involves leveraging synthetic data generation, data augmentation, and domain adaptation techniques to augment the training dataset and mitigate the constraints of limited training data. We also consider semi-supervised learning as a viable approach for demosaicing.

In our investigation of the semi-supervised representation-learning module, we delve into a novel form of representation termed “oriented line segments (OLS),” illustrated in Figure 2. The OLS introduces two key hyperparameters: the length of a line segment, denoted as (2l+1), and the angle formed between two consecutive line segments.

The computational complexity of advanced demosaicing algorithms presents another challenge, particularly for real-time applications on resource-constrained devices like mobile phones or embedded systems. To address this, we propose the utilization of regression-based methods. This involves extracting and processing small image patches during demosaicing to learn the ground [...]

Single image super-resolution (SISR) is an intensively studied topic in image processing. It aims at recovering a high-resolution (HR) image from its low- resolution (LR) counterpart. SISR finds wide real-world applications such as remote sensing, medical imaging, and biometric identification. Besides, it attracts attention due to its connection with other tasks (e.g., image registration, compression, and synthesis).To deal with such ill-posed problem, we recently proposed two methods, LSR[1] and LSR++[2], by providing reasonable performance and effectively reduced complexity. LSR consists of three cascaded modules:
1) Unsupervised Representation Learning by creating a pool of rich and diversified representations in the neighborhood of a target pixel,
2) Supervised Feature Learning by Relative Feature Test (RFT [3]) to select a subset from the representation pool that is most relevant to the underlying super-resolution task automatically, and
3) Supervised Decision Learning by predicting the residual of the target pixel based on the selected features through regression via classical machine learning, and effectively fusioning the predictions for more stable results. LSR++ is promoted based on LSR, with emphasis on sample alignment, a more promising sample preparation process which is suitable for all patch-based computer vision problems. As illustrated in Fig 1, based on gradient histograms of patches along the eight reference directions (Fig.1.a), patch alignment utilizes patch rotations and flipping to meet the standard templates of gradient histograms, where D_max is the direction with the largest cumulative gradient magnitude, and D_max_orth_b and D_max_orth_s refer to the orthogonal directions to D_max with big and small cumulative gradient magnitude, respectively. By modifying the set of (D_max, D_max_orth_b, and D_max_orth_s) of a patch, patch alignment can regularize the edge pattern with the patch by directions perpendicular the edge (D_max) and directions along [...]