Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

Performance on GradEva. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

The considered test problems. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Performance on FunEva. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Performance on Iter. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

Continuation of Table 2. by Jamilu Yahaya (18563445)

“…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”

A new fast filtering algorithm for a 3D point cloud based on RGB-D information by Chaochuan Jia (7256237)

“…Then, the optimal segmentation threshold of the V image that is calculated by using the Otsu algorithm is applied to segment the color mapping image into a binary image, which is used to extract the valid point cloud from the original point cloud with outliers. …”

Symmetry test result. by Jiyong Moon (16819302)

Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

Sample image for illustration. by Indhumathi S. (19173013)

“…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …”

Quadratic polynomial in 2D image plane. by Indhumathi S. (19173013)

“…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …”

Comparison analysis of computation time. by Indhumathi S. (19173013)

“…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …”

Process flow diagram of CBFD. by Indhumathi S. (19173013)

“…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …”

Precision recall curve. by Indhumathi S. (19173013)

“…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …”

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

Table_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Table_2_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Presentation_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Table_3_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.pdf by Wenchao Zhang (1324959)

Data_Sheet_1_Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.zip by Wenchao Zhang (1324959)