Bilal Taha

IEEE Access · 2019

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Bilal Taha trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Cemal Aker
2. Sinan Kalkan

Papers from Bilal Taha's perspective

Ordered by how strongly the researchers Bilal Taha trusts endorse each paper. Same corpus, a different order for every researcher.

1. Do we need hundreds of classifiers to solve real world classification problems?
2. Using deep networks for drone detection
3. Detection, Tracking, and Interdiction for Amateur Drones (IEEE Communications Magazine)
4. Histograms of oriented gradients for human detection
5. ImageNet Large Scale Visual Recognition Challenge
6. Video Google: a text retrieval approach to object matching in videos
7. Fast R-CNN
8. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
9. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
10. Object recognition from local scale-invariant features

Their papers

• Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research (2019) — 308 citations